SSLROC By Shijun Wang and Ronald M. Summers.
This reference must be cited when using this code: Shijun Wang, Diana Li, Nicholas Petrick, Berkman Sahiner, Marius George Linguraru, and Ronald M. Summers. Optimizing Area Under the ROC Curve Using Semi-Supervised Learning. Pattern Recognition, vol. 48(1), pp. 276-287, 2014.
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The minimum Matlab version required to run this package is Matlab version 7.12.0.0635 (R2011a).
Function experiment(method, dataset, P1, P2) is the main function of this package. Input parameter: method: ROC optimization method to be tested. dataset: designate which dataset will be used for the testing. P1 and P2: parameter used by the method tested.
Output Test results will be saved in the following file: [name_method '/' Para.Global.name 'AUC' name_method 'P1' str1 'P2' str2].txt
The proposed method for ROC optimization using semi-supervised learning is function [d_double, output] = ROC_SSL(Y_training, X_training, X_test, para) Input parameters: Y_training: labels of training samples X_training: features of training samples X_test: features of test samples with unknown labels para: parameters used to tune the performance of SSLROC algorithm
Data Format: Data set to be tested was pre-processed and stored in a Matlab *.dat data file. It contains X_ori and Y_ori, which corresponds to feature set and label set, respectively. Then we employ cross-validation method to split them into training and testing sets.
To run the package the compare the performance of SSLROC with other state-of-the-art ROC optimization methods, you may need to download the following third-party packages: LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/ Semi-supervised RankBoost for bipartite Ranking: https://www-connex.lip6.fr/~amini/SSRankBoost/
CSDP, A C Library for Semidefinite Programming: https://projects.coin-or.org/Csdp OPAUC: https://lamda.nju.edu.cn/code_OPAUC.ashx?AspxAutoDetectCookieSupport=1 SDPT3 version 4.0 -- a MATLAB software for semidefinite-quadratic-linear programming: https://www.math.nus.edu.sg/~mattohkc/sdpt3.html SeDuMi: https://sedumi.ie.lehigh.edu/ SVM and Kernel Methods Matlab Toolbox: https://asi.insa-rouen.fr/enseignants/~arakoto/toolbox/ YALMIP: https://users.isy.liu.se/johanl/yalmip/