Time related association rules mining for traffic prediction based on genetic network programming combined with estimation of distribution algorithms

Yang Wang, Shingo Mabu, Huiyu Zhou, Xianneng Li, Kaoru Shimada, Bofeng Zhang, Kotaro Hirasawa

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

Abstract

In this paper, a method of time-related class association rule mining is proposed based on Genetic Network Programming (GNP) combined with Estimation of Distribution Algorithms (EDAs). There are two important points in this paper: The first important point is to combine GNP with Estimation of Distribution Algorithms which are a novel evolution strategy. The second important point is that three kinds of probability models have been put forward for generating new individuals. The aim of this paper is to extract more interesting association rules and to improve the traffic prediction accuracy by combining Genetic Network Programming with Estimation of Distribution Algorithms. We applied the proposed data mining algorithm to traffic systems in order to predict the traffic volume in future. The simulation results show that our proposed method is effective compared with the conventional method based on GNP.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages3468-3473
Number of pages6
Publication statusPublished - 2009
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka
Duration: 2009 Aug 182009 Aug 21

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CityFukuoka
Period09/8/1809/8/21

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Keywords

  • Data mining
  • Estimation of Distribution Algorithms (EDAs)
  • Genetic Network Programming (GNP)

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Wang, Y., Mabu, S., Zhou, H., Li, X., Shimada, K., Zhang, B., & Hirasawa, K. (2009). Time related association rules mining for traffic prediction based on genetic network programming combined with estimation of distribution algorithms. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 3468-3473). [5335117]