Abstract
Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network (BN) based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
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This work was supported by National Natural Science Foundation of China (No. 60970055).
Qiang Lv graduated from Soochow University, PRC in 1988. He received the M. S. degree from China Eastern Institute of Technology in 1991 and the Ph.D. degree from Soochow University in 2006. He is currently a professor at the School of Computer Science and Technology, Soochow University.
His research interests include bioinformatics, meta heuristics search, and parallel and distributed computing.
Xiao-Yan Xia received the B. Sc. degree in computer science from the Soochow University, PRC in 2003. She is currently a research fellow of the Provincial Key Laboratory for Computer Information Processing Technology, Soochow University.
Her research interests include database system design and its application.
Pei-De Qian received the B. Sc. degree in computer science from Nanjing University, PRC in 1982. He is currently a professor at the School of Computer Science and Technology, Soochow University.
His research interests include Chinese information processing, distributed computing, and operating system.
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Lv, Q., Xia, XY. & Qian, PD. A fast calculation of metric scores for learning Bayesian network. Int. J. Autom. Comput. 9, 37–44 (2012). https://doi.org/10.1007/s11633-012-0614-8
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DOI: https://doi.org/10.1007/s11633-012-0614-8