A multi-objective artificial immune algorithm for parameter optimization in support vector machine

被引：154

作者：

Aydin, Ilhan ^{[1
]}

Karakose, Mehmet ^{[1
]}

Akin, Erhan ^{[1
]}

机构：

[1] Firat Univ, Dept Comp Engn, TR-23119 Elazig, Turkey

来源：

APPLIED SOFT COMPUTING | 2011年 / 11卷 / 01期

关键词：

Support vector machine; Artificial immune system; Optimization; Fault diagnosis; Anomaly detection; FAULT-DETECTION; PATTERN-RECOGNITION; DIAGNOSIS; SELECTION;

D O I：

10.1016/j.asoc.2009.11.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, sigma parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained. (c) 2009 Elsevier B.V. All rights reserved.

引用

页码：120 / 129

页数：10

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