A feature selection technique for classificatory analysis

Amir Ahmad, Lipika Dey

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)


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
Issue number1
Publication statusPublished - Jan 1 2005
Externally publishedYes


  • Classificatory knowledge extraction
  • Feature selection
  • Significance of attributes

ASJC Scopus subject areas

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


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