Toward integrating feature selection algorithms for classification and clustering

Huan Liu, Lei Yu

Research output: Contribution to journalArticlepeer-review

2225 Scopus citations


This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development.

Original languageEnglish (US)
Pages (from-to)491-502
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number4
StatePublished - Apr 2005


  • Categorizing framework
  • Classification
  • Clustering
  • Feature selection
  • Real-world applications
  • Unifying platform

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Toward integrating feature selection algorithms for classification and clustering'. Together they form a unique fingerprint.

Cite this