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 (Conference Proceeding)

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 '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery (ACM)
Pages1465-1472
Number of pages8
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - 6 Jul 2018
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period15/07/1819/07/18

Keywords

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

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