Finding the optimal number of features based on mutual information

P. Chen, A. Wilbik, S. van Loon, A.-K. Boer, U. Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
2 Downloads (Pure)

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
Title of host publicationAdvances in Fuzzy Logic and Technology 2017 - Proceedings of
Subtitle of host publicationEUSFLAT-2017 – The 10th Conference of the European Society for Fuzzy Logic and Technology, IWIFSGN’2017 – The 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets
PublisherSpringer
Pages477-486
Number of pages10
Volume641
ISBN (Print)9783319668291
DOIs
Publication statusPublished - 2018
Event10th Conference of the European Society for Fuzzy Logic and Technology, (EUSFLAT 2017) and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017, 11-15 September 2017, Warsaw, Poland - Warsaw, Poland
Duration: 11 Sept 201715 Sept 2017
http://www.eusflat2017.ibspan.waw.pl/

Publication series

NameAdvances in Intelligent Systems and Computing
Volume641
ISSN (Print)2194-5357

Conference

Conference10th Conference of the European Society for Fuzzy Logic and Technology, (EUSFLAT 2017) and 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN 2017, 11-15 September 2017, Warsaw, Poland
Abbreviated titleEUSFLAT2017
Country/TerritoryPoland
CityWarsaw
Period11/09/1715/09/17
Internet address

Keywords

  • Feature selection
  • Fuzzy models
  • Mutual information
  • Number of features

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