Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The topological fusion of Bayes nets
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommender systems for evaluating computer messages
Communications of the ACM
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
The fuzzy association degree in semantic data models
Fuzzy Sets and Systems
Constructing the Dependency Structure of a Multiagent Probabilistic Network
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Introduction to Probability with Statistical Applications
Introduction to Probability with Statistical Applications
Experimental study for the comparison of classifier combination methods
Pattern Recognition
Learning Bayesian network parameters under incomplete data with domain knowledge
Pattern Recognition
Information theoretic combination of pattern classifiers
Pattern Recognition
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Graphical representations of consensus belief
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A noise-detection based AdaBoost algorithm for mislabeled data
Pattern Recognition
Stochastic margin-based structure learning of Bayesian network classifiers
Pattern Recognition
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Effective knowledge integration plays a very important role in knowledge engineering and knowledge-based machine learning. The combination of Bayesian networks (BNs) has shown a promising technique in knowledge fusion and the way of combining BNs remains a challenging research topic. An effective method of BNs combination should not impose any particular constraints on the underlying BNs such that the method is applicable to a variety of knowledge engineering scenarios. In general, a sound method of BNs combination should satisfy three fundamental criteria, that is, avoiding cycles, preserving the conditional independencies of BN structures, and preserving the characteristics of individual BN parameters, respectively. However, none of the existing BNs combination method satisfies all the aforementioned criteria. Accordingly, there are only marginal theoretical contributions and limited practical values of previous research on BNs combination. In this paper, following the approach adopted by existing BNs combination methods, we assume that there is an ancestral ordering shared by individual BNs that helps avoid cycles. We first design and develop a novel BNs combination method that focuses on the following two aspects: (1) a generic method for combining BNs that does not impose any particular constraints on the underlying BNs, and (2) an effective approach ensuring that the last two criteria of BNs combination are satisfied. Further through a formal analysis, we compare the properties of the proposed method and that of three classical BNs combination methods, and hence to demonstrate the distinctive advantages of the proposed BNs combination method. Finally, we apply the proposed method in recommender systems for estimating users' ratings based on their implicit preferences, bank direct marketing for predicting clients' willingness of deposit subscription, and disease diagnosis for assessing patients' breast cancer risk.