Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening
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초록
Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalancean imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples forlearning classifiers. Safe sample screening can identify a part of non-informative samples and retain informativesamples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe SampleScreening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via aWeighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class,and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informativeminority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. Theexperimental results show that Re-SSS can effectively improve the classification performance of imbalancedclassification problems.
Abstract
Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalancean imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples forlearning classifiers. Safe sample screening can identify a part of non-informative samples and retain informativesamples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe SampleScreening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via aWeighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class,and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informativeminority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. Theexperimental results show that Re-SSS can effectively improve the classification performance of imbalancedclassification problems.
참고문헌 (32)
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