A feature selection technique for classificatory analysis

Amir Ahmad, Lipika Dey

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)

Abstract

Patterns summarizing mutual associations between class decisions and attribute values in a pre-classified database, provide insight into the significance of attributes and also useful classificatory knowledge. In this paper we have proposed a conditional probability based, efficient method to extract the significant attributes from a database. Reducing the feature set during pre-processing enhances the quality of knowledge extracted and also increases the speed of computation. Our method supports easy visualization of classificatory knowledge. A likelihood-based classification algorithm that uses this classificatory knowledge is also proposed. We have also shown how the classification methodology can be used for cost-sensitive learning where both accuracy and precision of prediction are important.

Original languageEnglish
Pages (from-to)43-56
Number of pages14
JournalPattern Recognition Letters
Volume26
Issue number1
DOIs
Publication statusPublished - Jan 1 2005
Externally publishedYes

Keywords

  • Classificatory knowledge extraction
  • Feature selection
  • Significance of attributes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A feature selection technique for classificatory analysis'. Together they form a unique fingerprint.

Cite this