Efficient 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 Elect & Informat Sci, Kure, Hiroshima 7378506, Japan

来源：

CCCT 2003, VOL 1, PROCEEDINGS: COMPUTING/INFORMATION SYSTEMS AND TECHNOLOGIES | 2003年

关键词：

emergent computing; on-line pruning; self-organization; ensemble learning; classification;

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：

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 an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. 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

页码：58 / 63

页数：6

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