A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization
| Autor: | Jun-Zhong Ji, Hong-Xun Zhang, Ren-Bing Hu, Chun-Nian Liu |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
business.industry
Heuristic (computer science) Materials Science (miscellaneous) Ant colony optimization algorithms MathematicsofComputing_NUMERICALANALYSIS Process (computing) Bayesian network Scale (descriptive set theory) Mutual information ComputingMethodologies_ARTIFICIALINTELLIGENCE General Business Management and Accounting Industrial and Manufacturing Engineering Convergence (routing) Artificial intelligence Business and International Management General Agricultural and Biological Sciences business Algorithm Metaheuristic Mathematics |
| Zdroj: | Acta Automatica Sinica. 35:281-288 |
| ISSN: | 1874-1029 |
| DOI: | 10.1016/s1874-1029(08)60077-4 |
| Popis: | To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization (I-ACO-B). First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided. And then, by combining the global score increase of a solution and local mutual information between nodes, a new heuristic function with better heuristic ability is given to induct the process of stochastic searches. The experimental results on the benchmark data sets show that the new algorithm is effective and efficient in large scale databases, and greatly enhances convergence speed compared to the original algorithm. |
| Databáze: | OpenAIRE |
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