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32nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Jose, Sonu Louis, Sushil J. Dascalu, Sergiu M. Liu, Siming Univ Nevada Comp Sci & Engn Reno NV 89557 USA

ISBN: (纸本)9781728192284

In this paper, we propose a new hybrid structure learning method that incorporates case-injected genetic algorithms as a score-and-search method for determining Bayesian network structure from data. In our approach, we first find the probabilistic dependencies among variables to constrain the search space and then employ case-injected genetic algorithms in the score-and-search phase to find a quality structure from the reduced search space. The novelty of our work lies with the introduction of combining case-based reasoning with genetic algorithms to evolve a near-optimal Bayesian network in fewer generations compared to a randomly initialized genetic algorithm. Our case-injected genetic algorithms enhance Bayesian network structure learning performance over a sequence of similar problems by extracting and storing knowledge from previously solved problems and utilizing the accumulated knowledge to solve subsequent similar problems. To evaluate the viability of our proposed approach, we conducted a series of experiments by generating a sequence of similar problems based on using data sets obtained randomly from three well-known benchmark Bayesian networks. We also compared the performance of our proposed approach with the state-of-the-art algorithm. Our preliminary results show that case-injected genetic algorithms provide better performance in learning Bayesian network structure compared to GA and the state-of-the-art algorithm. Our proposed approach has applications in real-world domains such as e-commerce system and health care.

关键词： Bayesian networks structure learning genetic algorithms case-injected genetic algorithms

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Transfer Learning-based Hybrid Approach for Bayesian Network Structure Learning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第7期31卷 2260003-2260003页

作者： Jose, Sonu Louis, Sushil Dascalu, Sergiu Liu, Siming Univ Nevada Dept Comp Sci & Engn 1664 N Virginia St Reno NV 89557 USA Missouri State Univ Dept Comp Sci & Engn 901 S Natl Ave Springfield MO 65897 USA

Bayesian network is a graphical model that is widely used to perform probabilistic reasoning. However, learning the structure of Bayesian network is a complex task. In this paper, we propose a hybrid structure learning algorithm that has two phases: a constraint-based phase to reduce the search space and a score-and-search phase that employs case-injected genetic algorithms for determining the optimal structure from the reduced space of structures. We use a case-injected genetic algorithm-based hybrid approach for the structure learning in order to improve the learning accuracy over similar problems. A case-injected genetic algorithm is the augmentation of a case-based memory with the genetic Algorithm (GA). Thereby, it finds near-optimal solutions in fewer generations compared to GA. Our method stores relevant or partial solutions in a case-base while solving the problems and utilizes those stored solutions on new similar problems. We use small-to-very large networks for assessing our viability of our approach. In this paper, a series of experiments are conducted on datasets generated from four benchmark Bayesian networks. We compare our method against GA-based hybrid approach and a state-of-the-art algorithm, Max-Min Hill Climbing (MMHC). Presented results indicate an enhanced improvement of our approach over GA and MMHC in learning the Bayesian network structures.

关键词： Bayesian network structure learning graphical models case-injected genetic algorithms genetic algorithms transfer learning

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