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- Title
Learning Bayesian Network Classifier Based on Artificial Fish Swarm Algorithm.
- Authors
Chun-Feng Wang; Kui Liu
- Abstract
Classification is an important task in data mining, which has been successfully applied to many areas. Bayesain network classifier aims to compute the class with the highest probability given a case. Since learning Bayesian network classifier from a dataset can be viewed as an optimization problem, heuristic algorithms may be used to find high-quality networks in medium or large scale problems. In this paper, we present a new artificial fish swarm algorithm for learning Bayesian network classifier. In this algorithm, an unconstrained optimization problem is established firstly. Its optimal solution is an undirected graph, which can be used to reduce the search space. Then, three behaviors of the artificial fish swarm are defined. Finally, the detailed description of the algorithm is given. In the experimental of the paper, the performance of the proposed algorithm is compared with other three classifiers. The results show that the proposed algorithm is effective.
- Subjects
BAYESIAN analysis; SWARM intelligence; DATA mining; HEURISTIC algorithms; MATHEMATICAL optimization
- Publication
IAENG International Journal of Computer Science, 2015, Vol 42, Issue 4, p355
- ISSN
1819-656X
- Publication type
Article