Bayesian network ensemble learning with inadequate information

2014 ◽  
Author(s):  
Ruijie Du ◽  
Shuangcheng Wang ◽  
Cuiping Leng ◽  
Yunbin Fu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Inadequate Information

scholarly journals A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2054
Author(s):  
Ming Li ◽  
Ren Zhang ◽  
Kefeng Liu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Causal Reasoning ◽  
Learning Algorithm ◽  
Training Sample ◽  
Causal Analysis ◽  
Local Optimum ◽  
Structural Learning ◽  
Ensemble Learning Algorithm ◽  
Bagging Method

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.

A Two-Stage Approach for Flight Departure Delay Forecasting Using Ensemble Learning

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Feng Guan ◽  
Mengyan Hao ◽  
Zhen Guo
Keyword(s):  
Ensemble Learning ◽  
Two Stage
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