Feature Selection for Classification Using an Ant System Approach

被引：0
|
作者：
Abd-Alsabour, Nadia [1 ]
机构：
[1] Bond Univ, Sch Informat Technol, Southport, Qld 4229, Australia
来源：
DISTRIBUTED, PARALLEL AND BIOLOGICALLY INSPIRED SYSTEMS | 2010年 / 329卷
关键词：
Ant colony optimization; pattern recognition; support vector machine and feature selection; COLONY OPTIMIZATION; VARIABLE SELECTION; QSAR;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many applications such as pattern recognition and data mining require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant, redundant or noisy features while keeping the most informative ones. In this paper, an ant system approach for solving feature selection for classification is presented. The results we got are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets.
引用
收藏
页码：233 / 241
页数：9
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