Self-organizing neural grove

被引：1

作者：

Inoue, Hirotaka ^{[1
]}

机构：

[1] Department of Electrical Engineering and Information Science, Kure National College of Technology, 2-2-11 Agaminami, Kure, 737-8506, Hiroshima

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8834卷

关键词：

D O I：

10.1007/978-3-319-12637-1_18

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 have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on SGNN increases in proportion to the numbers of SGNN. In this paper, we propose a novel pruning method for efficient classification and we call this model a self-organizing neural grove (SONG). Experiments have been conducted to compare the SONG with bagging and the SONG with boosting, and support vector machine (SVM). The results show that the SONG can improve its classification accuracy as well as reducing the computation cost. ©Springer International Publishing Switzerland 2014.

引用

页码：143 / 150

页数：7

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