Ensemble Learning (also known as Ensembling) is an exciting yet challenging field. Ensembling leverages multiple base models to achieve better predictive performance, which is often better than any of the constituent models alone [19]. It has been proven critical in many practical applications and data science competitions [4], e.g., Kaggle.

To promote the learning of ensembling, we create this repository with:

1. Books & Academic Papers
2. Online Courses and Videos
3. Open-source and Commercial Libraries/Toolboxes and Datasets
4. Key Conferences & Journals

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ ([email protected]). Enjoy reading!

• 1. Books & Tutorials
  • 1.1. Books
  • 1.2. Tutorials
• 2. Courses/Seminars/Videos
• 3. Toolboxes & Datasets
  • 3.1. Toolboxes
  • 3.2. Datasets
• 4. Papers
  • 4.1. Overview & Survey Papers
  • 4.2. Key Algorithms
  • 4.3. Boosting
  • 4.4. Clustering Ensemble
  • 4.5. Outlier Ensemble
  • 4.6. Ensemble Learning for Data Stream
• 5. Key Conferences/Workshops/Journals
  • 5.1. Conferences & Workshops
  • 5.2. Journals

Ensemble Methods: Foundations and Algorithms by Zhi-Hua Zhou [30]: Classical text book covering most of the ensemble learning techniques. A must-read for people in the field. [Full Book]

Ensemble Machine Learning: Methods and Applications edited by Oleg Okun [28]: Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs.

Applications of Supervised and Unsupervised Ensemble Methods edited by Oleg Okun [17]: This book contains the extended papers presented at the 2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA), in conjunction with ECAI’2008.

Data Mining and Knowledge Discovery Handbook Chapter 45 (Ensemble Methods for Classifiers): by Lior Rokach [22]: This chapter provides an overview of ensemble methods in classification tasks. We present all important types of ensemble method including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.

Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe [2]: Great intro book for ensemble learning in outlier analysis.

Coursera - How to Win a Data Science Competition: Learn from Top Kagglers:

• Ensembling (92 mins)

Coursera - Machine Learning: Classification by University of Washington partly covers the topic:

• Ensemble classifiers
• Ensembles, Bagging, Boosting

Machine Learning and Data Mining by Prof. Alexander Ihler: Section on ensembling (4 videos).

[Python] combo: combo is a comprehensive Python toolbox for combining machine learning (ML) models and scores for various tasks, including classification, clustering, and anomaly detection. It supports the combination of ML models from core libraries such as scikit-learn and xgboost (documentation).

[Python] pycobra: python library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.

[Python] DESlib: A Python library for dynamic classifier and ensemble selection.

[Python] imbalanced-learn: A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning (documentation).

As a subfield of machine learning, ensemble learning is usually tested against general machine learning benchmark datasets. Some helpful links can be found below:

• List of datasets for machine-learning research - Wikipedia
• UCI Machine Learning Repository
• PMLB: a large benchmark suite for machine learning evaluation and comparison [18]: Dataset Repository

Key data mining conference deadlines, historical acceptance rates, and more can be found data-mining-conferences.

ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)

ACM International Conference on Management of Data (SIGMOD)

The Web Conference (WWW)

IEEE International Conference on Data Mining (ICDM)

SIAM International Conference on Data Mining (SDM)

IEEE International Conference on Data Engineering (ICDE)

ACM InternationalConference on Information and Knowledge Management (CIKM)

ACM International Conference on Web Search and Data Mining (WSDM)

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

ACM Transactions on Knowledge Discovery from Data (TKDD)

IEEE Transactions on Knowledge and Data Engineering (TKDE)

ACM SIGKDD Explorations Newsletter

Data Mining and Knowledge Discovery

Knowledge and Information Systems (KAIS)