An evolutionary modular neural network for unbalanced pattern classifications

被引：1
|
作者：
Zhao, Zhong-Qiu [1 ]
Huang, De-Shuang [1 ]
机构：
[1] Chinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Anhui, Peoples R China
来源：
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/CEC.2007.4424673
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an evolutionary modular neural network is proposed to solve multi-class problems with unbalanced training sets. The proposed model can transform an unbalanced classification problem into a set of symmetrical two-class problems, each of which can be solved by a single simple neural network. The experimental results show that the proposed method reduces time consumption for training and improves the classification performance.
引用
收藏
页码：1662 / 1669
页数：8
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