Improved heuristic equivalent search algorithm based on Maximal Information Coefficient for Bayesian Network Structure Learning

被引：31
作者：
Zhang, Yinghua [1 ]
Zhang, Wensheng [1 ]
Xie, Yuan [1 ]
机构：
[1] Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
来源：
NEUROCOMPUTING | 2013年 / 117卷
基金：
美国国家科学基金会;
关键词：
Bayesian network; Structure learning; Maximal Information Coefficient; Heuristic Search; PROBABILISTIC NETWORKS; BELIEF NETWORKS; DIAGNOSIS; SYSTEM;
D O I：
10.1016/j.neucom.2013.02.015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Greedy Equivalent Search (GES) is an effective algorithm for Bayesian network structure learning problem, which searches in the space of graph equivalence classes. However, original GES which takes greedy strategy into account may easily fall into local optimization trap because of the empty initial structure. In this paper, an improved GES method is proposed. It firstly designs a draft of the real network, based on conditional independence tests and Maximum Information Coefficient, which helps in finding more correct dependent relationship between variables. To ensure correctness, this draft is used as a seed structure of original GES algorithm. Numerical experiments on four standard networks show that SCo (the value of the BDeu score) and NEtoGS (the number of graph structure, which is equivalent to the Gold Standard network) have big improvement. Also, the total of learning time is greatly reduced. Therefore, our improved method can relatively quickly determine the structure with highest degree of data matching. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：186 / 195
页数：10
相关论文
共 35 条
[1]  
Agosta J., 1990, 4 WORKSH UNC ART INT, P1
[2]  
Andersson SA, 1997, SCAND J STAT, V24, P81
[3]  
Beinlich I., 1989, P 2 EUR C AI MED
[4]   Bayes prediction for the number of failures of a repairable system [J].
Beiser, JA ;
Rigdon, SE .
IEEE TRANSACTIONS ON RELIABILITY, 1997, 46 (02) :291-295
[5]   Adaptive probabilistic networks with hidden variables [J].
Binder, J ;
Koller, D ;
Russell, S ;
Kanazawa, K .
MACHINE LEARNING, 1997, 29 (2-3) :213-244
[6]  
Bonissone P.P., 1991, EQUIVALENCE SYNTHESI
[7]   Improving Bayesian network structure learning with mutual information-based node ordering in the K2 algorithm [J].
Chen, Xue-Wen ;
Anantha, Gopalakrishna ;
Lin, Xiaotong .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2008, 20 (05) :628-640
[8]  
Cheng J., 1997, Proceedings of the sixth international conference on Information and knowledge management, Association for Computing Machinery, P325, DOI [10.1145/266714.266920, DOI 10.1145/266714.266920]
[9]  
Chickering D. M., 2003, Journal of Machine Learning Research, V3, P507, DOI 10.1162/153244303321897717
[10]  
Chickering David Maxwell, 1996, Learning from data: Artificial intelligence and statistics V, P121
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