A novel entropy-based approach to feature selection

Chia Hao Tu, Chunshien Li

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


The amount of features in datasets has increased significantly in the age of big data. Processing such datasets requires an enormous amount of computing power, which exceeds the capability of traditional machines. Based on mutual information and selection gain, the novel feature selection approach is proposed. With Mackey-Glass, S&P 500, and TAIEX time series datasets, we investigated how good the proposed approach could perform feature selection for a compact subset of feature variables optimal or near optimal, through comparing the results by the proposed approach to those by the brute force method. With these results, we determine the proposed approach can establish a subset solution optimal or near optimal to the problem of feature selection with very fast calculation.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
EditorsSatoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319544717
StatePublished - 2017
Event9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
Duration: 3 Apr 20175 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10191 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017


  • Feature selection
  • Information entropy
  • Probability density estimation
  • Time series dataset


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