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

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

Information, Production and Systems Research Center

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages	3468-3473
Number of pages	6
Publication status	Published - 2009
Event	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka Duration: 2009 Aug 18 → 2009 Aug 21

Other

Other	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
City	Fukuoka
Period	09/8/18 → 09/8/21

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

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Time related association rules mining for traffic prediction based on genetic network programming combined with estimation of distribution algorithms. / Wang, Yang; Mabu, Shingo; Zhou, Huiyu; Li, Xianneng; Shimada, Kaoru; Zhang, Bofeng; Hirasawa, Kotaro.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 3468-3473 5335117.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 5335117, pp. 3468-3473, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, 09/8/18.

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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.",

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AU - Wang, Yang

AU - Mabu, Shingo

AU - Zhou, Huiyu

AU - Li, Xianneng

AU - Shimada, Kaoru

AU - Zhang, Bofeng

AU - Hirasawa, Kotaro

PY - 2009

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N2 - 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.

AB - 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.

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KW - Estimation of Distribution Algorithms (EDAs)

KW - Genetic Network Programming (GNP)

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