Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/416591 |
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| _version_ | 1832567175996506112 |
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| author | Mingzhu Tang Chunhua Yang Kang Zhang Qiyue Xie |
| author_facet | Mingzhu Tang Chunhua Yang Kang Zhang Qiyue Xie |
| author_sort | Mingzhu Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-74fefc27312245a48eb23678774967f3 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-74fefc27312245a48eb23678774967f32025-02-03T01:02:14ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/416591416591Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data ClassificationMingzhu Tang0Chunhua Yang1Kang Zhang2Qiyue Xie3School of Energy and Power Engineering, Changsha University of Science & Engineering, Changsha 410114, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Energy and Power Engineering, Changsha University of Science & Engineering, Changsha 410114, ChinaSchool of Energy and Power Engineering, Changsha University of Science & Engineering, Changsha 410114, ChinaCost-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.http://dx.doi.org/10.1155/2014/416591 |
| spellingShingle | Mingzhu Tang Chunhua Yang Kang Zhang Qiyue Xie Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification Abstract and Applied Analysis |
| title | Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification |
| title_full | Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification |
| title_fullStr | Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification |
| title_full_unstemmed | Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification |
| title_short | Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification |
| title_sort | cost sensitive support vector machine using randomized dual coordinate descent method for big class imbalanced data classification |
| url | http://dx.doi.org/10.1155/2014/416591 |
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