文章基本信息

标题：Feature selected cost-sensitive twin SVM for imbalanced data
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作者：Xiaopeng Li ; Xianrong Zhang
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2020
卷号：309
页码：1-6
DOI：10.1051/matecconf/202030905013
语种：English
出版社：EDP Sciences
摘要：In this paper, we propose a cost-sensitive twin SVM (cs-tsvm) and apply it to imbalanced data. A weight is added to each instance according to its cost of misclassification which is related to its position. In preprocessing part, features are selected by their difference of majority and minority classes. The feature is selected when its difference value is higher than average one. The experiment is conducted on UCI datasets and G-mean, AUC and accuracy are evaluation metrics. The experimental results show that Feature selection with CS-TWSVM is useful for datasets with high dimension.
关键词：Keywords:enTwin SVMcost-sensitivefeature selectionimbalanced data