Evolutionary multi-class support vector machines for classification

被引：0

作者：

Stoean, Ruxandra ^{[1
]}

Stoean, Catalin ^{[1
]}

Preuss, Mike ^{[2
]}

Dumitrescu, Dan ^{[3
]}

机构：

[1] Univ Craiova, Dept Comp Sci, Craiova, Romania

[2] Univ Dortmund, Dept Comp Sci, D-44227 Dortmund, Germany

[3] Univ Babes Bolyai, Dept Comp Sci, Cluj Napoca 400084, Romania

来源：

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL | 2006年 / 1卷

关键词：

evolutionary support vector machines; multi-class classification; one-against-one method; Iris data set;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Evolutionary support vector machines represent a new learning technique that we recently developed as a hybridization between support vector machines and evolutionary algorithms, regarding the discovery of the optimal decision function within the former. The new approach has proven to be successful as binary classification problems have been concerned. Present paper presents the extension of the aforementioned technique to the. more frequent case of multi-class classification. Validation of evolutionary multi-class support vector machines is performed on the well-known benchmark problem of Fisher's Iris plants classification and results demonstrate the promise of the new approach.

引用

页码：423 / 428

页数：6

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