Feature Selection for Support Vector Machines Base on Modified Artificial Fish Swarm Algorithm

被引：5
|
作者：
Lin, Kuan-Cheng [1 ]
Chen, Sih-Yang [1 ]
Hung, Jason C. [2 ]
机构：
[1] Natl Chung Hsing Univ, Dept Management Informat Syst, Taichung 40227, Taiwan
[2] Overseas Chinese Univ, Dept Informat Management, Taichung 40721, Taiwan
来源：
UBIQUITOUS COMPUTING APPLICATION AND WIRELESS SENSOR | 2015年 / 331卷
关键词：
Artificial fish swarm algorithm; Feature selection; Support vector machine; Swarm intelligence; CLASSIFICATION;
D O I：
10.1007/978-94-017-9618-7_28
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Feature selection is a search process to find the optimal feature subset to describe the characteristics of dataset exactly. Artificial Fish Swarm Algorithm is a novel meta-heuristic search algorithm, which can solve the problem of optimization by simulate the behaviors of fish swarm. This study proposes a modified version of Artificial Fish Swarm Algorithm to select the optimal feature subset to improve the classification accuracy for Support Vector Machines. The empirical results showed that the performance of the proposed method was superior to that of basic version of Artificial Fish Swarm Algorithm.
引用
收藏
页码：297 / 304
页数：8
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