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A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm

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  • Intelligent and Information Systems
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Abstract

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, 134, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea

    Jaehun Lee, Wooyong Chung & Euntai Kim

  2. Network Sys. Div. Internet Infra Team, Samsung Electronics, 416, Matan-3Dong, Suwon, 443-742, Korea

    Soohan Kim

Authors
  1. Jaehun Lee
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  2. Wooyong Chung
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  3. Euntai Kim
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  4. Soohan Kim
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Corresponding author

Correspondence to Euntai Kim.

Additional information

Recommended by Editorial Board member Sungshin Kim under the direction of Editor Young-Hoon Joo. This work was supported by the Ministry of Commerce, Industry and Energy of Korea (HISP). E. Kim appreciates the financial support from LG Yonam Foundation during his sabbatical year at the University of California, Berkeley.

Jaehun Lee received his B.S. and M.S. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2005 and 2007, respectively. He is currently a Ph.D. candidate of the School of Electrical and Electronic Engineering at Yonsei University. His current research interests include computational intelligence, localization and tracking in wireless sensor network.

Wooyong Chung received his B.S. and M.S. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2004 and 2006, respectively. He is currently a Ph.D. candidate of the School of Electrical and Electronic Engineering at Yonsei University. His current research interests include fuzzy control and evolutionary algorithm.

Euntai Kim received his B.S. (with top honors), M.S., and Ph.D. degrees in Electronic Engineering from Yonsei University, Seoul, Korea, in 1992, 1994, and 1999, respectively. From 1999 to 2002, he was a full-time lecturer with the Department of Control and Instrumentation Engineering at Hankyong National University, Gyeonggi-do, Korea. Since 2002, he has been with the School of Electrical and Electronic Engineering at Yonsei University, where he is currently an Associate Professor. He was a Visiting Scholar with the University of Alberta, Edmonton, Canada, and the Berkeley Initiative in Soft Computing (BISC), UC Berkeley, USA, in 2003 and 2008, respectively. His current research interests include computational intelligence and machine learning and their application to intelligent service robots, unmanned vehicles, home networks, biometrics, and evolvable hardware.

Soohan Kim received his B.S. degrees in Material Science from Chonnam National University, Gwangju, Korea, in 1990. He is currently a Senior Manager and Project Leader of the Internet Infra Technical Planning Team at Samsung Electronics Co. in Korea HQ. He joined Samsung in 1993 and was the first Software Development manager. His current research interests include Contents Sharing with 3 Screens, Multi-Media Platforms, localization and tracking in home network.

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Lee, J., Chung, W., Kim, E. et al. A new genetic approach for structure learning of Bayesian networks: Matrix genetic algorithm. Int. J. Control Autom. Syst. 8, 398–407 (2010). https://doi.org/10.1007/s12555-010-0227-3

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  • DOI: https://doi.org/10.1007/s12555-010-0227-3

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