Ensemble Methods

We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants to the "prior knowledge" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants to the "agnostic learning" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performances. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: https://www.agnostic.inf.ethz.ch/ .

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia-the state of Victoria. The field data were obtained using the 'habitat hectares' approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.

Over the past few years, there has been a renewed interest in the consensus clustering problem. Several new methods have been proposed for finding a consensus partition for a set of n data objects that optimally summarizes an ensemble. In this paper, we propose new consensus clustering algorithms with linear computational complexity in n. We consider clusterings generated with a random number of clusters, which we describe by categorical random variables. We introduce the idea of cumulative voting as a solution for the problem of cluster label alignment, where unlike the common one-to-one voting scheme, a probabilistic mapping is computed. We seek a first summary of the ensemble that minimizes the average squared distance between the mapped partitions and the optimal representation of the ensemble, where the selection criterion of the reference clustering is defined based on maximizing the information content as measured by the entropy. We describe cumulative vote weighting schemes and corresponding algorithms to compute an empirical probability distribution summarizing the ensemble. Given the arbitrary number of clusters of the input partitions, we formulate the problem of extracting the optimal consensus as that of finding a compressed summary of the estimated distribution that preserves the maximum relevant information. An efficient solution is obtained using an agglomerative algorithm that minimizes the average generalized Jensen-Shannon divergence within the cluster. The empirical study demonstrates significant gains in accuracy and superior performance compared to several recent consensus clustering algorithms.

3rd International Conference on NLP & Artificial Intelligence Techniques (NLAI 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP & Artificial Intelligence Techniques. The Conference looks for significant contributions to all major fields of NLP and Artificial Intelligence in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Wireless network has an exponential increase in various aspects of the human community. Accordingly, transmitting a vast volume of sensitive and non-sensitive data over the network puts them at risk of being attacked. To avoid this, Intrusion Detection System (IDS) security is intended to detect threats and protect devices from attacks. IDS usually uses one of the following alternative approaches: signature-based, anomaly-based, or hybrid of the two. In spite of the IDS has been the focus of much research in recent years, there is still space for improvement. Based on the anomalybased approach, this paper proposes a modified algorithm called a Multi-layer Feature Selection and Reduction IDS (MFSR-IDS) for providing high-level protection against Denial-of-Service (DoS) and Probe attacks. The MFSR-IDS framework makes three major contributions. First, it reduces the feature dimensionality of the network dataset across three layers. Second, it has a fast and accurate detection system. Third, it provides a mathematical model of the framework under consideration. The MFSR-IDS algorithm selects optimal number of features from KDDCUP'99 dataset which used to train the predictive model based on different learning classifiers and ensemble methodology. The performance of MFSR-IDS is evaluated in terms of Detection Rate (DR), False Positive Rate (FPR), FScore, ROC area, Accuracy (Acc) and Processing time. The experiments indicate that, the proposed MFSR-IDS outperforms some existing IDS frameworks in terms of DR, FPR, Acc and Processing time in detecting DoS and Probe attacks.

Incremental learning is a machine learning paradigm where the learning process takes place whenever new example/s emerge and adjusts what has been learned according to the new example/s. The most prominent difference of incremental learning from traditional machine learning is that it does not assume the availability of a sufficient training set before the learning process, but the training examples appear over time. In this paper we discuss the methods of incremental learning which are currently available. This paper gives the overview of the current research in the incremental learning which will be beneficial to the research scalars.

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company's daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.

Understanding what aspects of the urban environment are associated with better socioeconomic/liveability outcomes is a long standing research topic. Several quantitative studies have investigated such relationships. However, most of such works analysed single correlations, thus failing to obtain a more complete picture of how the urban environment can contribute to explain the observed phenomena. More recently, multivariate models have been suggested. However, they use a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity and independence among regressors. In this paper, we propose a quantitative methodology to study the relationship between a more comprehensive set of metrics of the urban environment and the valorisation of street segments that handles non-linearity and possible interactions among variables, through the use of Machine Learning (ML). The proposed methodology was tested on the French Riviera and outputs show a moderate predictive capacity (i.e., adjusted R 2 = 0.75) and insightful explanations on the nuanced relationships between selected features of the urban environment and street values. These findings are clearly location specific; however, the methodology is replicable and can thus inspire future research of this kind in different geographic contexts.

Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors-Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms-Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regression, Lasso Regression, and Elastic Net Regression has been used to combine the advance regression techniques. Tree-based algorithms generate a decision tree from categorical 'YES' and 'NO' values, Ensemble methods to boosting up the learning and prediction accuracy, Support Vector Machine to extend the model for both classification and regression approach and lastly advance linear regression to predict the house price with different features values.

This paper is prepared to provide a brief introduction to the topic of Ensemble Learning. It aims to provide the reader with a broad overview on the approach of Ensemble Methods.

Sections:

-What is Ensemble Learning?

-The Rationale Behind Ensemble Methods

-Common Approaches To Ensemble Methods

-Success Factors Of Ensemble Methods

-Proven Applications of Ensemble Methods

Note: This paper is part of EVOLUTIQ's educational series on the subject of systematic trading. These papers will discuss different approaches to systematic trading as well as present specific trading strategies and associated risk management techniques.

Flood susceptibility Decision tree (DT) Ensemble GIS Remote sensing Malaysia s u m m a r y Flood is one of the natural hazards which occur all over the world and it is critical to be controlled through proper management. Severe flood events in Kelantan, Malaysia cause damage to both life and property every year, and therefore the development of flood model to recognize the susceptible areas in watersheds is important for decision makers. Remote sensing (RS) and geographic information system (GIS) techniques could be useful in hydrological studies while they are able to fulfill all the requirements for comprehensive, rapid and accurate analysis. The aim of the current research is to compare the prediction performances of two different approaches such as rule-based decision tree (DT) and combination of frequency ratio (FR) and logistic regression (LR) statistical methods for flood susceptibility mapping at Kelantan, Malaysia. DT is based on the rules which are created precisely and strongly by considering all the characteristics of the variables which can enhance the performance of the flood susceptibility mapping. On the other hand, LR as multivariate statistical analysis (MSA) has some weak points. For that reason, FR was used to analyze the impact of classes of each variable on flood occurrence and overcome the weakness of LR. At first, flood inventory map with a total of 155 flood locations was extracted from various sources over the part of the Kelantan. Then the flood inventory data was randomly divided into a testing dataset 70% (115 flood locations) for training the models and the remaining 30% (40 flood locations) was used for validation purpose. The spatial database includes digital elevation model (DEM), curvature, geology, river, stream power index (SPI), rainfall, land use/cover (LULC), soil type, topographic wetness index (TWI) and slope. For validation both success and prediction rate curves were performed. The validation results showed that, area under the curve for the results of DT and integrated method of FR and LR was 87% and 90% for success rate and 82% and 83% for prediction rate respectively.

With the intent of helping to reconceptualize music teacher education programs and improve the quality of music education for all students, the purpose of this study was to examine the interactions within the cultural cohort communities of a music teacher education program embedded within the culture of a school of music and the role that these interactions play in “disrupting,” or challenging, pre-service educators’ occupational identities and preconceptions of music education. Research questions for this explanatory sequential (Creswell & Plano Clark, 2011) mixed methods study focused on determining which structural components of a music teacher education program (e.g., course curricula, University/College of Education/School of Music/music education program requirements, music education program sequence), if any, assist with “disrupting” pre-service music educators’ occupational identities; why certain communities, interactions, and persons within the cultural cohort are the most significant “disruptive” influences on pre-service music educators’ occupational identities; and how these “disruptions” manifested themselves in pre-service music educators’ conceptions of and beliefs and attitudes about music education.
Participants completed a Pre-Service Music Educator Survey to help indicate which courses, persons, social interactions, or other influences within or outside of the School of Music most influenced them to change their beliefs of music education and identities as music educators between matriculation into the School of Music and the time of taking the survey. Following data collection and analysis of the Pre-Service Music Educator Survey, I created cohort groups of students from classes that were identified by undergraduate music education students as being highly “disruptive” to participate in focus group discussions. Additionally, all music education faculty members participated in a focus group discussion and each participated in an individual interview.
Results from the survey suggested changes in students’ occupational identities and preconceptions of music education during their time in the music teacher education program, changes in interests in occupational types of music teaching (e.g., early childhood music educator, high school performance-based ensemble teacher, etc.), and changes in means of teaching music (e.g., performance-based ensembles, teaching elementary general music, music technology, popular music, etc.). Additionally, focus group discussion revealed that several components of the music teacher education program are “disruptive” influences on students’ occupational identities and preconceptions of music education, including a “de-tracked” music teacher education curriculum, two introductory/foundational music education courses during the first and second years of study, and required fieldwork experiences embedded in most music education courses. Interactions within the “community” nature of the program between peers, faculty, and graduate student assistants also were “disruptive” influences.
Implications for music teacher education include creating opportunities for philosophical discussions regarding contemporary topics in music education throughout a music teacher education program, devoting time during music education courses for students to examine the present state of their occupational identities, facilitating fieldwork experiences in “disruptive” settings, and striving to create a sense of “community” within the music teacher education program.

İleri yaşlarda meydana gelen obezite hastalığının temelinin çocukluk yıllarındaki beslenme ve yaşam alışkanlıklarıyla ilgili olduğu bilimsel çalışmalarla tespit edilmiştir. Çalışmamız çocuklarda obeziteye yakalanma riskini hesaplayan bir erken uyarı sistemi üzerinedir. Makine öğrenmesi kolektif öğrenme algoritmaları (Ensemble Methods) kullanılarak yapay ve özgün bir klinik karar destek sistemi geliştirilmiştir. Bu amaçla çalışmamızda kullanılmak üzere hazırlanan bir anket ile devlet hastanelerinden ve okullarından veriler toplanarak bir eğitim seti oluşturulmuştur. Eğitim seti üzerinde kolektif öğrenme algoritmalarıyla yapılan öğrenme ve çapraz geçerleme işlemlerinde başarılı sonuçlar elde edilmiştir.

A comprehensive landslide susceptibility mapping (LSM) should be produced to reduce damages to individuals and infrastructures. In the international landslide literature, various statistical methods such as logistic regression (LR) and frequency ratio (FR) have been widely used individually for LSM. However, these methods have some weaknesses. This study aims to propose an ensemble method of FR and LR to overcome their weak points. This ensemble method was applied on Fayfa area which is located in the southwest of Saudi Arabia (SA) in Jazan region. The study area covers an area of *260 km2, and is located between latitudes 17_1104600N to 17_2101600N and longitudes 43_003000E to 43_1103800E. For LSM, an inventory map with 86 landslide occurrences was extracted from various sources. Then, the landslide inventory was randomly divided into two datasets: 70 % for training the models and 30 % for validation. The landslide conditioning factors used in the LSL include altitude, curvature, distance from wadis, distance from road, distance from fault, stream power index, topographic wetness index, soil type, geology, slope, and aspect. The produced susceptibility maps were validated by an area under the curve technique. The success rate curves of the models measure the goodness of fit of the training data, while the prediction rate curves provide the validation of the susceptibility maps, measuring the prediction capability. The validation results showed 65, 69, and 79 % success rates for FR, LR, and ensemble models, respectively. Furthermore, the prediction rates were 58, 77, and 82 % for FR, LR, and ensemble models, respectively. The proposed ensemble method can be replicated in other natural hazard studies because it can produce accurate assessment for disaster management and decision making.

Species distribution models are a key component for understanding a species' potential occurrence, specifically in vastly undersampled landscapes. The current species distribution data for the Assamese macaque Macaca assamensis are outdated, but suggest a patchy distribution in moist broadleaved forests in South and Southeast Asia. Therefore, in this study, we used a species distribution model to explore the potential climatic niche of this species and assess its distribution and potential barriers in 12 South and Southeast Asian countries. We combined primary and secondary species occurrence records from different countries. We applied Classification and Regression Tree (CART), TreeNet (boosting), RandomForest (bagging) and Maximum Entropy (MaxEnt) machine-learning algorithms with elevation as well as 19 bioclimatic variables for the first ensemble predictions ever completed for this species. Our results suggested that the predicted distribution of the Assamese macaque is strongly associated with precipitation of warmest quarter (BIO18), temperature annual range (BIO7) and temperature seasonality (BIO4). Our prediction shows a continuous potential climatic niche of the species from east of the Kaligandaki River in Nepal to Lao People's Democratic Republic. There are also potential niche patches in Bhutan, Southeast China, Thailand and Cambodia, while Pakistan and Afghanistan have no potential niche for the species. We believe that our workflow presents a new conservationoriented open access research template to progress empirical primate conservation worldwide.

This paper tackles the problem of integrating household energy prosumers in Smart Energy Grids by analyzing a set of state-of-the-art energy forecasting techniques that allow individual or aggregated prosumers to evaluate their future energy demand and inform the Distributed System Operator (DSO) about potential grid imbalances. Thus, the DSO can perform a proactive strategy to manage the grid and avoid problems before they appear. The key element of this approach is the prediction technique, that must be accurate enough such that the resulting grid imbalances can be compensated in real-time. The paper evaluates a set of state-of-the-art statistical and Machine Learning (ML) prediction techniques, such as SARIMA, feed-forward and recurrent neural networks, support vector regression or ensemble prediction models, on real household historical energy demand logs by performing a feature selection process for each ML algorithm as to identify the best elements that influence the energy demand of a house. A set of experiments are performed on the REFIT Electrical Load Measurements data set evaluating each model's performance with respect to the selected features. Among the evaluated algorithms, the Ensemble Prediction Model gives best prediction accuracy, showing a Mean Absolute Percentage Error (MAPE) of 14.4% followed by the SVM model with a MAPE of 15.4%.

will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its advances. The Conference looks for significant contributions to all major fields of the Artificial Intelligence in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

—In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation algorithm for combining the ranks of the individual feature selection methods of the ensemble. This algorithm uses the rank order along with the rank score of the features in the ranked list of each feature selection method for rank aggregation. The ensemble of multiple feature selection techniques uses the novel rank aggregation algorithm and selects the relevant features using the 80%, 60%, 40% and 20% thresholds from the top of the aggregated ranked list for building the C4.5, MLP, C4.5 based Bagging and MLP based Bagging models. It was observed that the performance of models using the ensemble of multiple feature selection techniques is better than the performance of 5 individual rank based feature selection methods. The average performance of all the models was observed as best for the ensemble of feature selection techniques at 60% threshold. Also, the bagging based models outperformed the individual models most significantly for the 60% threshold. This increase in performance is more significant from the fact that the number of features were reduced by 40% for building the highest performing models. This reduces the data dimensions and hence the overall data size phenomenally for model building. The use of the ensemble of feature selection techniques using the novel aggregation algorithm provided more accurate models which are simpler, faster and easy to interpret.

Landslide susceptibility mapping is indispensable for disaster management and planning development operations in mountainous regions. The potential use of light detection and ranging (LiDAR) data was explored in this study for deriving landslide-conditioning factors for the spatial prediction of landslide-susceptible areas in a landslide-prone area in Ulu Klang, Malaysia. Nine landslide-conditioning factors, such as altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), sediment transport index (STI), and slope length (SL), were directly derived from LiDAR for landslide-susceptibility mapping. The main objective of this research was to propose a novel ensemble landslide susceptibility mapping method to enhance the performance of individual methods of support vector machine (SVM) and evidential belief function (EBF). SVM is time-consuming when various data types, such as nominal, scale, and ordinal, are used. This characteristic of the individual SVM method is not optimal for hazard modeling. This drawback can be resolved by assessing the effects of the classes of each conditioning factor on landslide occurrence through a datadriven EBF method. Hence, EBF was applied in this study, and weights were acquired for the classes of each conditioning factor. The conditioning factors were reclassified based on the attained weights and entered into SVM as a scale to evaluate the correlation between landslide occurrence and each conditioning factor. Four SVM kernel types [radial basis function kernel (RBF), sigmoid kernel (SIG), linear kernel (LN), and polynomial kernel (PL)] were tested to explore the efficiency of each kernel in SVM modeling. The efficiencies of the ensemble EBF and SVM methods were examined through area under curve (AUC). The RBF kernel obtained better results than the other kernel types. The success and prediction rates obtained from the validation results of ensemble EBF and RBF-SVM method were 83.04% and 80.04%, respectively. The proposed novel ensemble method reasonably accelerated the processing and enhanced the results by combining the advantages of both methods.

Mixture of Experts (ME) is a modular neural network architecture for supervised learning. In this paper, we propose an evidence-based ME to deal with the classification problem. In the basic form of ME the problem space is automatically divided into several subspaces for the experts and the outputs of experts are combined by a gating network. Satisfactory performance of the basic ME depends on the diversity among experts. In conventional ME, different initialization of experts and supervision of the gating network during the learning procedure , provide the diversity. The main idea of our proposed method is to employ the Dempster-Shafer (D-S) theory of evidence to improve determination of learning parameters (which results more diverse experts) and the way of combining experts' decisions. Experimental results with some data sets from UCI repository show that our proposed method yields better classification rates as compared to basic ME and static combining of neural network based on D-S theory.

3rd International Conference on Machine Learning and Soft Computing (MLSC 2022) will
provide an excellent international forum for sharing knowledge and results in theory, methodology
and applications Machine learning and Soft Computing. The aim of the conference is to provide a
platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field.

Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the areas of Machine learning, Soft Computing and applications.

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.

Arrhythmia is an abnormal condition of the heart that occurs when the electrical impulses that coordinate the heartbeats do not work properly, causing the heart to beat too fast, too slow, irregular or even have premature contractions; accurate and quick diagnosis can reduce fatalities, Thispaper focuses on design and development of a system that will automatically and accurately diagnose five super-class of arrhythmias recommended by theAssociation for the Advancement of Medical Instrumentation (AAMI) to be detectable by equipment/methodswhich includes: Normal(N), Supraventricular ectopicbeat (SVEB), Ventricular ectopic beat (VEB), Fusion beat (F) and Unknown beat (Q). To achieve this, the system uses a hybrid classification algorithm achieved by the combination of two better performing algorithm (Random Forest and J.48 ensembled with the stacking algorithm). The MIT-BIH ECG arrhythmia database accessible at Kaggle.com was used for training, testing and validation of the system. since this design is intended for edge devices or mobile devices, the design focuses on development of a system that uses less computation power, less application size and ability to give correct classification with less time. Our results showed that the designed system has an overall accuracy of 97.67%, average precision value of 0.977, average recall value of 0.977 and average F1-measure value of 0.977.

Parallel to the rapid technological advances, up-to-date remote sensing platforms and sensors have made it possible to observe the Earth's surface features at a higher spatial and spectral resolution. The WorldView-2 (WV-2) imagery has been effectively used for the detailed mapping of agricultural crop-type types in many studies. The selected area for this study covers approximately 17 km 2 of various agricultural land and forest areas. Hazelnut and corn products, which are spectrally similar crop types, are the most dominant and economically valuable agricultural products for this particular region. Therefore, accurate determination of these agricultural products and mapping of their spatial locations play important role for yield estimation. The primary objective of this paper is to map cultivated areas by classifying a WV-2 imagery using conventional classifiers and advanced ensemble learning algorithms. In addition, several spectral indices were used as an ancillary dataset to identify and differentiate cultivated areas and forest species from each other. Within this context, object-based image analysis (OBIA) with multi-resolution segmentation was performed to produce image objects. Then, a total of 22 image object subsets were determined for the segmented image objects. Four classification algorithms, namely Random Forest (RF), Canonical Correlation Forest (CCF), Decision Tree (DT), and k-nearest neighbor (k-NN) classifiers were applied to produce the thematic map of the study area. Result of the study showed that the CCF classifier reached the highest overall accuracy of 94% with dataset having 22 object subsets. The improvement in classification performance reached to 7% in terms of overall classification accuracy. Moreover, the results noticeably indicated that ensemble methods (i.e., RF and CCF) outperformed the DT and k-NN classifiers in terms of applied accuracy measures. The results were confirmed by the McNemar's statistical test. Moreover, feature importance results of the RF algorithm showed that the most important vegetation indices were Chlorophyll RedEdge, green leaf, and NDVI-2 indices, respectively.

3rd International Conference on Advances in Artificial Intelligence Techniques (ArIT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its advances. The Conference looks for significant contributions to all major fields of the Artificial Intelligence in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

A generalisation of bottom-up pruning is proposed as a model level combination method for a decision tree ensemble. Bottom up pruning on a single tree involves choosing between a subtree rooted at a node, and a leaf, dependant on a pruning criterion. A natural extension to an ensemble of trees is to allow subtrees from other ensemble trees to be grafted onto a node in addition to the operations of pruning to a leaf and leaving the existing subtree intact. Suitable pruning criteria are proposed and tested for this multitree pruning context. Gains in both performance and in particular compactness over individually pruned trees are observed in tests performed on a number of datasets from the UCI database. The method is further illustrated on a churn prediction problem in the telecommunications domain.

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90±0.01 is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms.

Time-frequency and timescale analysis Dual-tree complex wavelet transform Denoising Ensemble methods Support vector machines Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders in auscultation. Crackles are very common adventitious transient sounds. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases may be assessed. In this study, a method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and timescale analysis from pulmonary signals. In order to understand the effect of using different window and wavelet types in time-frequency and timescale analysis in detecting crackles, different windows and wavelets are tested such as Gaussian, Blackman, Hanning, Hamming, Bartlett, Triangular and Rectangular windows for time-frequency analysis and Morlet, Mexican Hat and Paul wavelets for timescale analysis. The extracted feature sets, both individually and as an ensemble of networks, are fed into three different machine learning algorithms: Support Vector Machines, k-Nearest Neighbor and Multilayer Perceptron. Moreover, in order to improve the success of the model, prior to the time-frequency/scale analysis, frequency bands containing no-crackle information are removed using dual-tree complex wavelet transform, which is a shift invariant transform with limited redundancy compared to the conventional discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets, which are extracted using different window and wavelet types, for both pre-processed and non-pre-processed data with different machine learning algorithms, are extensively evaluated and compared.

ABSTRACT: Species distribution models are a key component for understanding a species’ potential
occurrence, specifically in vastly undersampled landscapes. The current species distribution
data for the Assamese macaque Macaca assamensis are outdated, but suggest a patchy distribution
in moist broadleaved forests in South and Southeast Asia. Therefore, in this study, we used a
species distribution model to explore the potential climatic niche of this species and assess its
distribution and potential barriers in 12 South and Southeast Asian countries. We combined
primary and secondary species occurrence records from different countries. We applied Classification
and Regression Tree (CART), TreeNet (boosting), RandomForest (bagging) and Maximum
Entropy (MaxEnt) machine-learning algorithms with elevation as well as 19 bioclimatic variables
for the first ensemble predictions ever completed for this species. Our results suggested that the
predicted distribution of the Assamese macaque is strongly associated with precipitation of
warmest quarter (BIO18), temperature annual range (BIO7) and temperature seasonality (BIO4).
Our prediction shows a continuous potential climatic niche of the species from east of the Kaligandaki
River in Nepal to Lao People’s Democratic Republic. There are also potential niche patches
in Bhutan, Southeast China, Thailand and Cambodia, while Pakistan and Afghanistan have no
potential niche for the species. We believe that our workflow presents a new conservationoriented
open access research template to progress empirical primate conservation worldwide.

In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to the other methods.

This research proposes a new feature selection algorithm, Class-specific Ensemble Feature Selection (CEFS), which finds class-specific subsets of features optimal to each available classification in the dataset. Each subset is then combined with a classifier to create an ensemble feature selection model which is further used to predict unseen instances. CEFS attempts to provide the diversity and base classifier disagreement sought after in effective ensemble models by providing highly useful, yet highly exclusive feature subsets. Also, the use of a wrapper method gives each subset the chance to perform optimally under the respective base classifier. Preliminary experiments implementing this innovative approach suggest potential improvements of more than 10% over existing methods.

Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on
ME, different strategies were developed to divide the problem space between the experts. To survey and analyse these methods more clearly, we present a categorisation of the ME literature based on this difference. Various ME implementations were classified into two groups, according to the partitioning strategies used and both how and when the gating network is involved in the partitioning and combining procedures. In the first group, The conventional ME and the extensions of this method stochastically partition the problem space into a number of subspaces using a special employed error function, and experts become specialised in each subspace. In the second group, the problem space is explicitly partitioned by the clustering method before the experts’ training process starts, and each expert is then assigned
to one of these sub-spaces. Based on the implicit problem space partitioning using a tacit competitive process between the experts, we call the first group the mixture of implicitly
localised experts (MILE), and the second group is called mixture of explicitly localised experts (MELE), as it uses pre-specified clusters. The properties of both groups are investigated
in comparison with each other. Investigation of MILE versus MELE, discussing the advantages and disadvantages of each group, showed that the two approaches have complementary
features.Moreover, the features of the ME method are compared with other popular combining methods, including boosting and negative correlation learning methods. As the
investigated methods have complementary strengths and limitations, previous researches that attempted to combine their features in integrated approaches are reviewed and, moreover, some suggestions are proposed for future research directions.

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms.

Ensemble learning refers to a collection of methods that learn a target function by training a number of individual learners and combining their prediction. We explore dif erent ensemble methods in boosting like AdaBoost, CatBoost, Light Boost, and XGBoost by combining their outputs, for classification and regression problems. When compare with single classifiers like Navies Bayes, Decision trees, Support Vector machines, Ensemble methods are more accurate in decision making. In this research, the dataset is school asset dataset, where the major target attributes are latitude, longitude of the school and population in one particular community. Based on the childrens population, the available school assets are requisite for the community or not. Many classification and ensemble algorithms are used to map the school assets in the community and boosting algorithms gives maximum accuracy in mapping the assets. Experiment is conducted in the Asset dataset and results according to classification parameters such as accuracy and AUC are presented.

Özetçe-Etiketli eğitim verisi temin etmek uzun süren maliyetli bir iştir. Aktif öğrenme, makine öğrenmesi algoritmalarının makul başarı oranlarına daha az etiketli eğitim örneği ile ulaşabilmesini amaçlar. Bu amaçla öncelikli olarak hangi örneklerin etiketlerine ihtiyaç duyulduğunun belirlenmesinde kullanılan yöntemlerden biri sınıflandırıcı topluluklarının kararlarından faydalanmaktır. Çalışma kapsamında topluluk tabanlı bir aktif öğrenme uygulaması gerçeklenmiştir ve performansı aktif olmayan öğrenme yöntemleriyle kıyaslanmıştır.

The purpose of this paper is to evaluate and benchmark ensemble methods for time series prediction for daily currency exchange rates using ensemble feedforward neural networks and kernel partial least squares (K-PLS). Best-practice forecasting methods for the US Dollar (USD) per Indian Rupee (IR) are applied for training, validating, and testing the machine learning models. In order to perform the benchmarking evaluation study neural network forecasting methods are first compared on a benchmarked neural network time series prediction method for the Canadian Lynx time series. The K-PLS method is benchmarked in addition with support vector machines (SVM), a similar kernel-based method. Both one-step ahead and a rollout methods for extended forecast horizons are applied for the currency exchange rates. The paper is novel in the sense that two new ensemble methods are introduced: weight seeding and multiple cross-validation averaging. The paper is also novel in the sense that several new validation indices are proposed that are especially applicable for time series: q 2 and Q 2 and the fraction of misses in the exchange rate return space, which is a more relevant metric for currency speculation. As a general conclusion it is found that the USD per IR is quite predictable, while other currencies such as the USD per Euro and the Australian Dollar (AUD) per Euro are not predictable. Index Terms-Ensemble methods, time series, time series forecasting, currency exchange rates, neural networks, SVM, K-PLS, kernel partial least squares, Canadian lynx trapping.

Özetçe-Topluluk algoritmalarının başarıları iki temel ölçüte dayanır. İlki topluluk içindeki temel sınıflandırıcıların başarıları, ikincisi ise temel öğrenicilerin kararlarının birbirlerinden farklılığıdır. Rastgele Altuzaylar, yüksek farklılık üreterek başarılı olmaktadırlar. Bu çalışmada ise bu farklılığı daha da arttıracak bir yöntem önerilmiş ve 36 sınıflandırma veri kümesi üzerinde orijinal Rastgele Altuzaylarla karşılaştırılmıştır. Denemeler sonucunda önerilen Yarı Rastgele Altuzayların daha başarılı olduğu ancak başarı artışının temel öğrenici sayısıyla ters orantılı olduğu görülmüştür. Bu durum, az sayıda temel öğrenicinin kullanılacağı uygulamalar için Yarı Rastgele Altuzayların iyi bir tercih olduğunu göstermektedir.