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Bayesian network structure learning with improved genetic algorithm.
- Source :
- International Journal of Intelligent Systems; Sep2022, Vol. 37 Issue 9, p6023-6047, 25p
- Publication Year :
- 2022
-
Abstract
- As an important model of machine learning, Bayesian networks (BNs) have received a lot of attentions since they can be used for classification via probabilistic inference. However, since it is a complicated combination optimization problem, BN structure learning cannot be solved with classic convex optimization algorithms. Hence, evolutionary algorithms provide an alternative way to find a global solution to BN structure learning problem. In this paper, we improve the biased random-key genetic algorithm to solve the BN structure learning problem. Meanwhile, we apply a local optimization model as its decoder to improve the performance of the proposed algorithm. Finally, we conduct our experiments on nine benchmark networks and a real dataset of cross-site scripting (XSS) attack. Experimental results show that the proposed algorithm can obtain more accurate solutions than other state-of-the-art algorithms and achieve a good performance in XSS attack detection for web security. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08848173
- Volume :
- 37
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- International Journal of Intelligent Systems
- Publication Type :
- Academic Journal
- Accession number :
- 158876438
- Full Text :
- https://doi.org/10.1002/int.22833