Abstract
In this paper, we propose a more efficient Bayesian network structure learning algorithm under the framework of score based local learning (SLL). Our algorithm significantly improves computational efficiency by restricting the neighbors of each variable to a small subset of candidates and storing necessary information to uncover the spouses, at the same time guaranteeing to find the optimal neighbor set in the same sense as SLL. The algorithm is theoretically sound in the sense that it is optimal in the limit of large sample size. Empirical results testify its improved speed without loss of quality in the learned structures.
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Jianjun Yang is a PhD candidate in the Center for Information Science at Peking University, China. His research focus centers around theoretical and applied machine learning. His theoretical interests are in probabilistic graphical models and high dimensional statistics.
Yunhai Tong received his BS and MS in computer science from Petroleum University of China, in 1993 and 1996 respectively. He received his PhD in computer science from Peking University, China in 2002. He is currently an associate professor in the Department of Machine Intelligence and the Key Laboratory of Machine Perception (Ministry of Education) at Peking University, China. His current research interests include data warehousing, online analytical processing, and data mining.
Zitian Wang received his PhD in electrical engineering from Peking University, China in 2009. He is a researcher in the Agricultural Bank of China. He is interested in the application of novel algorithms of artificial intelligence in investment, banking, and management.
Shaohua Tan received his PhD in electrical engineering from Katholieke Universiteit Leuven, Belgium in 1987. He has been professor in Center for Information Science, Peking University, China for 13 years. He has held various teaching and research positions in a number of countries prior to joining Peking University. His research interests include developing qualitative modeling techniques in modeling complex real-world systems and analysis of financial systems using AI techniques.
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Yang, J., Tong, Y., Wang, Z. et al. Efficient and effective Bayesian network local structure learning. Front. Comput. Sci. 8, 527–536 (2014). https://doi.org/10.1007/s11704-014-3335-z
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DOI: https://doi.org/10.1007/s11704-014-3335-z