Abstract and Applied Analysis

Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification

Mingzhu Tang, Chunhua Yang, Kang Zhang, and Qiyue Xie

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Cost-sensitive support vector machine is one of the most popular tools to deal with class-imbalanced problem such as fault diagnosis. However, such data appear with a huge number of examples as well as features. Aiming at class-imbalanced problem on big data, a cost-sensitive support vector machine using randomized dual coordinate descent method (CSVM-RDCD) is proposed in this paper. The solution of concerned subproblem at each iteration is derived in closed form and the computational cost is decreased through the accelerating strategy and cheap computation. The four constrained conditions of CSVM-RDCD are derived. Experimental results illustrate that the proposed method increases recognition rates of positive class and reduces average misclassification costs on real big class-imbalanced data.

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Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 416591, 9 pages.

First available in Project Euclid: 6 October 2014

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Tang, Mingzhu; Yang, Chunhua; Zhang, Kang; Xie, Qiyue. Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 416591, 9 pages. doi:10.1155/2014/416591. https://projecteuclid.org/euclid.aaa/1412606361

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