Abstract
The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.
Highlights
College of Meteorology and Oceanography, National University of Defense Technology, Collaborative Innovation Center on Meteorological Disaster Forecast, Warning and Assessment, Nanjing University of Information Science and Engineering, Nanjing 210044, China
Aiming at the causality of Bayesian Network (BN), we introduce the causal Information Flow to conduct causal analysis of network structures and calculate weights for structure integration, which will be presented in the subsection
Causal Information Flow (IF) is introduced to construct a new integration rule, which fully takes the causality of BN into consideration
Summary
College of Meteorology and Oceanography, National University of Defense Technology, Collaborative Innovation Center on Meteorological Disaster Forecast, Warning and Assessment, Nanjing University of Information Science and Engineering, Nanjing 210044, China. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. A new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. C-EL has high accuracy and a strong generalization ability. It is capable of learning more accurate structures under the small training sample condition. In the era of Big Data, in order to obtain the scientific evaluation and decision, mathematical modeling for knowledge expression and inference, such as data mining and analysis, plays a crucial role, especially for complex problems with uncertain information and causal relationships.
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