Effective online pruning method for ensemble self-generating neural networks

被引：0
|
作者：
Inoue, H [1 ]
Narihisa, H [1 ]
机构：
[1] Kure Natl Coll Technol, Dept Elect Engn & Informat Sci, Kure, Hiroshima 7378506, Japan
来源：
2004 47TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL III, CONFERENCE PROCEEDINGS | 2004年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose a novel pruning method for the structure of the SGNN in the MCS. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.
引用
收藏
页码：85 / 88
页数：4
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