A fast calculation of metric scores for learning Bayesian network

Authors:
Qiang Lv;Xiao-Yan Xia;Pei-De Qian
Affiliations:
School of Computer Science and Technology, Soochow University, Suzhou, PRC 215006 and Jiangsu Provincial Key Lab for Computer Information Processing Technology, Suzhou, PRC 215006;School of Computer Science and Technology, Soochow University, Suzhou, PRC 215006 and Jiangsu Provincial Key Lab for Computer Information Processing Technology, Suzhou, PRC 215006;School of Computer Science and Technology, Soochow University, Suzhou, PRC 215006 and Jiangsu Provincial Key Lab for Computer Information Processing Technology, Suzhou, PRC 215006
Venue:
International Journal of Automation and Computing
Year:
2012

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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.