Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining

Fumito Uwano, Keiki Takadama, Koji Dobashi, Tim Kovacs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes high-dimensional data mining technique by integrating two data mining methods: Accuracy-based Learning Classifier Systems (XCS) and Random Forests (RF). Concretely, the proposed system integrates RF and XCS: RF generates several numbers of decision trees, and XCS generalizes the rules converted from the decision trees. The convert manner is as follows: (1) the branch node of the decision tree becomes the attribute; (2) if the branch node does not exist, the attribute of that becomes # for XCS; and (3) One decision tree becomes one rule at least. Note that # can become any value in the attribute. From the experiments of Multiplexer problems, we derive that: (i) the good performance of the proposed system; and (ii) RF helps XCS to acquire optimal solutions as knowledge by generating appropriately generalized rules.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1465-1472
Number of pages8
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - Jul 6 2018
Externally publishedYes
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: Jul 15 2018Jul 19 2018

Publication series

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Other

Other2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period7/15/187/19/18

Keywords

  • Accuracy-based Learning Classifier System
  • Data mining
  • High-dimensional data
  • Random Forest

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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