Feature selection by genetic algorithm that adopts the principle of margin-maximization and its application to character recognition

Akira Suzuki, Satoshi Shimada, Yoshimasa Kimura, Shunichi Yonemura, Masashi Morimoto

Research output: Contribution to journalArticle

Abstract

This paper proposes a feature selection method that improves the recognition rate significantly for not only training samples but also unknown samples by using the principle of margin-maximization in the support vector machine (SVM). SVM is well-known as a recognition method that can discriminate unknown samples with high precision, so feature selection with high recognition rates for unknown samples can be expected by adopting the principle of margin-maximization, the technical basis of SVM. We perform experiments on five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method improves the recognition rate significantly for not only training samples but also the unknown samples as expected.

Original languageEnglish
Pages (from-to)131-139
Number of pages9
JournalJournal of the Institute of Image Electronics Engineers of Japan
Volume41
Issue number2
Publication statusPublished - 2012

Keywords

  • Character recognition
  • Feature selection
  • Genetic algorithms
  • Margin maximization
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Feature selection by genetic algorithm that adopts the principle of margin-maximization and its application to character recognition'. Together they form a unique fingerprint.

  • Cite this