Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification
Joint Authors
Yang, Chunhua
Tang, Mingzhu
Zhang, Kang
Xie, Qiyue
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-03
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tang, Mingzhu& Yang, Chunhua& Zhang, Kang& Xie, Qiyue. 2014. Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1033749
Modern Language Association (MLA)
Tang, Mingzhu…[et al.]. Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1033749
American Medical Association (AMA)
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. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1033749
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033749