Heuristic Ensemble of Filters for Reliable Feature Selection

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1 Citation (Scopus)


Feature selection has become ever more important in data mining in recent years due to the rapid increase in the dimensionality of data. Filters are preferable in practical applications as they are much faster than wrapper based approaches, but their reliability and consistency vary considerably on different data and yet no rule exists to indicate which one should be used for a particular given dataset. In this paper, we propose a heuristic ensemble approach that combines multiple filters with heuristic rules to improve the overall performance. It consists of two types of filters: subset filters and ranking filters, and a heuristic consensus algorithm. The experimental results demonstrate that our ensemble algorithm is more reliable and effective than individual filters as the features selected by the ensemble consistently achieve better accuracy for typical classifiers on various datasets.
Original languageEnglish
Publication statusPublished - May 2014
EventInternational Conference on Pattern Recognition Applications and Methods (ICPRAM 2014) - , France
Duration: 10 May 2014 → …


ConferenceInternational Conference on Pattern Recognition Applications and Methods (ICPRAM 2014)
Period10/05/14 → …


  • Feature Selection
  • Filter
  • ensemble

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