Finding the Optimal Number of Features Based on Mutual Information

Peipei Chen*, Anna Wilbik, Saskia van Loon, Arjen-Kars Boer, Uzay Kaymak

*Corresponding author for this work

Research output: Contribution to conferencePaperAcademic

Abstract

For high dimensional data analytics, feature selection is an indispensable preprocessing step to reduce dimensionality and keep the simplicity and interpretability of models. This is particularly important for fuzzy modeling since fuzzy models are widely recognized for their transparency and interpretability. Despite the substantial work on feature selection, there is little research on determining the optimal number of features for a task. In this paper, we propose a method to help find the optimal number of feature effectively based on mutual information.
Original languageEnglish
Pages477-486
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventProceedings of the Conference of the European Society for Fuzzy Logic and Technology, International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets - Warsaw, Poland
Duration: 11 Sep 201715 Sep 2017

Conference

ConferenceProceedings of the Conference of the European Society for Fuzzy Logic and Technology, International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets
Abbreviated titleEUSFLAT 2017, IWIFSGN 2017
Country/TerritoryPoland
CityWarsaw
Period11/09/1715/09/17

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