Weighted Relaxed Support Vector Machines

Keywords

Classification; Cost-sensitive learning; Imbalanced data; Outliers; Relaxed support vector machines

Abstract

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.

Publication Date

2-1-2017

Publication Title

Annals of Operations Research

Volume

249

Issue

1-2

Number of Pages

235-271

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10479-014-1711-6

Socpus ID

85011407741 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85011407741

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