Binary particle swarm optimization in classification

被引：0

作者：

Cervantes, A ^{[1
]}

Galván, I ^{[1
]}

Isasi, P ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Dept Comp Sci, Madrid 28911, Spain

来源：

NEURAL NETWORK WORLD | 2005年 / 15卷 / 03期

关键词：

evolutionary computation; particle swarm optimization; pattern classification; swarm intelligence;

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：

Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of some well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rule's that map binary masks (that represent the attribute values), to the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, to compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.

引用

页码：229 / 241

页数：13

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