Learning Functional Dependency Networks Based on Genetic Programming
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
IEICE - Transactions on Information and Systems
Bayesian network structure learning using cooperative coevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Intelligent steganalytic system: application on natural language environment
WSEAS Transactions on Systems and Control
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Diagnose the mild cognitive impairment by constructing Bayesian network with missing data
Expert Systems with Applications: An International Journal
Data Mining and Knowledge Discovery
Constructing the Bayesian network structure from dependencies implied in multiple relational schemas
Expert Systems with Applications: An International Journal
A hybrid method for learning Bayesian networks based on ant colony optimization
Applied Soft Computing
Efficient learning bayesian networks using PSO
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the naïve Bayesian classifiers, and the tree-augmented naïve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.