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

Abstract and Applied Analysis

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

Mathematics

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