A semi-supervised feature selection algorithm for imbalanced data
DU Limin
XU Yang
Abstract：Considering the scarcity of labeled samples and the high feature dimension for imbalanced data,a new semi-supervised feature selection algorithm based on GA and Biased-SVM is proposed.The biased-SVM model which can dispose the unbalanced samples data is trained by the initial labeled sample set and then the trained Biased-SVM model is used to add labels to the unlabeled samples,and add the new labeled samples to the initial labeled sample set.Finally,the optimal feature subset is selected by the GA-based feature selection method for imbalanced data.Experimental results show that the proposed method not only reduces the feature dimension,but also improves the precision of the minor class under the different labeled sample rates generally.
Keywords：genetic algorithmBiased-SVMimbalanced datasemi-supervised learnfeature selection
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 95-99,105 )