Weighted relaxed support vector machines

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• 作者：Onur Şeref ; Talayeh Razzaghi ; Petros Xanthopoulos
• 关键词：Classification ; Cost ; sensitive learning ; Relaxed support vector machines ; Imbalanced data ; Outliers
• 刊名：Annals of Operations Research
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：249
• 期：1-2
• 页码：235-271
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：Operation Research/Decision Theory; Combinatorics; Theory of Computation;
• 出版者：Springer US
• ISSN：1572-9338
• 卷排序：249

文摘

Classification of imbalanced data is challenging when outliers exist. In this paper, we propose a supervised learning method to simultaneously classify imbalanced data and reduce the influence of outliers. The proposed method is a cost-sensitive extension of the relaxed support vector machines (RSVM), where the restricted penalty free-slack is split independently between the two classes in proportion to the number samples in each class with different weights, hence given the name weighted relaxed support vector machines (WRSVM). We compare classification results of WRSVM with SVM, WSVM and RSVM on public benchmark datasets with imbalanced classes and outlier noise, and show that WRSVM produces more accurate and robust classification results.