{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:40:35Z","timestamp":1787017235369,"version":"build-2736575974"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,11,1]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1142\/s021821302260003x","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T02:29:26Z","timestamp":1654482566000},"source":"Crossref","is-referenced-by-count":7,"title":["Transfer Learning-based Hybrid Approach for Bayesian Network Structure Learning"],"prefix":"10.1142","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5999-501X","authenticated-orcid":false,"given":"Sonu","family":"Jose","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, Nevada 89557, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushil","family":"Louis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, Nevada 89557, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergiu","family":"Dascalu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, Nevada 89557, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Missouri State University, 901 S National Ave, Springfield, MO 65897, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2022,6,6]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021821302260003X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T05:22:44Z","timestamp":1669094564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S021821302260003X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"journal-issue":{"issue":"07","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.1142\/S021821302260003X"],"URL":"https:\/\/doi.org\/10.1142\/s021821302260003x","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"article-number":"2260003"}}