Fuzzy criteria for feature selection

S.M. Vieira, J.M. Costa Sousa, da, U. Kaymak

Research output: Contribution to journalArticleAcademicpeer-review

55 Citations (Scopus)
5 Downloads (Pure)


The presence of less relevant or highly correlated features often decrease classification accuracy. Feature selection in which most informative variables are selected for model generation is an important step in data-driven modeling. In feature selection, one often tries to satisfy multiple criteria such as feature discriminating power, model performance or subset cardinality. Therefore, a multi-objective formulation of the feature selection problem is more appropriate. In this paper, we propose to use fuzzy criteria in feature selection by using a fuzzy decision making framework. This formulation allows for a more flexible definition of the goals in feature selection, and avoids the problem of weighting different goals is classical multi-objective optimization. The optimization problem is solved using an ant colony optimization algorithm proposed in our previous work. We illustrate the added value of the approach by applying our proposed fuzzy feature selection algorithm to eight benchmark problems.
Original languageEnglish
Pages (from-to)1-18
JournalFuzzy Sets and Systems
Issue number1
Publication statusPublished - 2012


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