Incremental Feature Selection

Huan Liu, Rudy Setiono

Research output: Contribution to journalArticle

85 Scopus citations

Abstract

Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.

Original languageEnglish (US)
Pages (from-to)217-230
Number of pages14
JournalApplied Intelligence
Volume9
Issue number3
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

Keywords

  • Dimensionality reduction
  • Feature selection
  • Machine learning
  • Pattern recognition

ASJC Scopus subject areas

  • Artificial Intelligence

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