Self-organizing neural grove

Author

Inoue, H. ; Sugiyama, Kiyotaka

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Kure Nat. Coll. of Technol., Hiroshima, Japan

fYear

2013

fDate

12-14 Sept. 2013

Firstpage

319

Lastpage

323

Abstract

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, the multiple classifier system based on C4.5, and support vector machine (SVM). The results show that the SONG can improve its classification accuracy as well as reducing the computation cost.

Keywords

learning (artificial intelligence); neural nets; pattern classification; support vector machines; SGNN; SONG model; SONG with bagging; SONG with boosting; SVM; classification accuracy; learning ability; multiple classifier systems; pruning method; self-generating neural networks; self-organizing neural grove model; support vector machine; Accuracy; Bagging; Boosting; Data mining; Memory management; Neural networks; Support vector machines; bagging; boosting; neural network ensembles; self-organization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2013 IEEE 7th International Conference on

Conference_Location

Berlin

Print_ISBN

978-1-4799-1426-5

Type

conf

DOI

10.1109/IDAACS.2013.6662697

Filename

6662697