A Classification Method Based on Improved Quantum-behaved Particle Swarm Optimization

被引：0
|
作者：
Zhang, Yugang [1 ]
Xiao, Shisong [1 ]
Liu, Wei [1 ]
Li, Xiaoxu [1 ]
机构：
[1] Huazhong Normal Univ, Dept Comp Sci, Wuhan 430079, Hubei, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL PRE-OLYMPIC CONGRESS ON COMPUTER SCIENCE, VOL II: INFORMATION SCIENCE AND ENGINEERING | 2008年
关键词：
Data mining; Quantum-behaved particle swarm; Classification; Rule extraction;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper introduces the "Absorbing Walls" and the multi-phase theory to improve Quantum-behaved Particle Swarm Optimization algorithm and applies it to solve complex problem of classification. In the process of classification, the double fitness functions are used to increase the precise of classification rules got. The result of experimentation shows that not only the efficiency of this improved optimization algorithm is increased, but also the accuracy rate of classification is high.
引用
收藏
页码：421 / 425
页数：5
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