Optimized Feature Selection Approach Based on Entropy for Multi-Class Data Classification

Krisyesika Krisyesika*, Joko Lianto Buliali, Ahmad Saikhu

*Corresponding author for this work

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

Abstract

Efficient machine learning heavily relies on solving the critical issue of feature selection, which serves as a valuable pre-processing method for improving data quality. Classification models face significant challenges, particularly when confronted with data containing irrelevant and redundant features. Unfortunately, only a few techniques specifically address feature selection in datasets with multi-class attributes. This research paper introduces a novel approach for feature selection called TSEFS, which utilizes entropy-based techniques such as fuzzy entropy and mutual information. TSEFS employs a two-stage process to select relevant features. The experimental results on three datasets demonstrate that the proposed method outperformed the existing feature selection methods.

Original languageEnglish
Title of host publicationProceeding - EECSI 2023
Subtitle of host publication10th Electrical Engineering, Computer Science and Informatics Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages349-354
Number of pages6
ISBN (Electronic)9798350306866
DOIs
Publication statusPublished - 2023
Event10th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2023 - Palembang, Indonesia
Duration: 20 Sept 202321 Sept 2023

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
ISSN (Print)2407-439X

Conference

Conference10th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2023
Country/TerritoryIndonesia
CityPalembang
Period20/09/2321/09/23

Keywords

  • feature selection
  • fuzzy entropy
  • information theory
  • multi-class classification
  • mutual information

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