A methodology for improving the performance of non-ranker feature selection filters

Lior Rokach, Barak Chizi, Oded Maimon

Research output: Contribution to journalArticlepeer-review

Abstract

Feature selection is the process of identifying relevant features in the dataset and discarding everything else as irrelevant and redundant. Since feature selection reduces the dimensionality of the data, it enables the learning algorithms to operate more effectively and rapidly. In some cases, classification performance can be improved; in other instances, the obtained classifier is more compact and can be easily interpreted. There is much work done on feature selection methods for creating ensemble of classifiers. Thus, these works examine how feature selection can help ensemble of classifiers to gain diversity. This paper examines a different direction, i.e. whether ensemble methodology can be used for improving feature selection performance. In this paper we present a general framework for creating several feature subsets and then combine them into a single subset. Theoretical and empirical results presented in this paper validate the hypothesis that this approach can help to find a better feature subset.

Original languageEnglish
Pages (from-to)809-830
Number of pages22
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume21
Issue number5
DOIs
StatePublished - Aug 2007

Keywords

  • Ensemble methodology
  • Feature selection
  • Pattern classification

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