A Task Decomposition Algorithm Based on the Distribution of Input Pattern Vectors for Classification Problems

Seiji Ishihara, Harukazu Igarashi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes an algorithm for decomposing a multi-class classification problem into a set of two-class classification problems. The algorithm divides a set of input pattern vectors corresponding to each class into subsets according to the distribution of the selected input pattern vectors. The distribution is represented by Gaussian mixture models which are estimated by EM algorithm with MDL criterion. In this paper, the algorithm applied for constructing a modular neural network. Experimental results showed that the algorithm simplifies multi-class classification problems efficiently.

Original languageEnglish
Pages (from-to)1043-1048
Number of pages6
JournalIEEJ Transactions on Electronics, Information and Systems
Volume125
Issue number7
DOIs
Publication statusPublished - 2005

Keywords

  • classification problem
  • EM algorithm
  • minimum description length criterion
  • modular neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A Task Decomposition Algorithm Based on the Distribution of Input Pattern Vectors for Classification Problems'. Together they form a unique fingerprint.

Cite this