Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sangmin | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-08-30T21:29:45Z | - |
dc.date.available | 2021-08-30T21:29:45Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-06-01 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/55073 | - |
dc.description.abstract | We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.subject | INFORMATION | - |
dc.title | Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1109/TKDE.2019.2899096 | - |
dc.identifier.scopusid | 2-s2.0-85084736586 | - |
dc.identifier.wosid | 000531656700001 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.32, no.6, pp.1157 - 1166 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.citation.title | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.citation.volume | 32 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1157 | - |
dc.citation.endPage | 1166 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordAuthor | Simulated annealing | - |
dc.subject.keywordAuthor | Markov processes | - |
dc.subject.keywordAuthor | Greedy algorithms | - |
dc.subject.keywordAuthor | Bayes methods | - |
dc.subject.keywordAuthor | Search problems | - |
dc.subject.keywordAuthor | Convergence | - |
dc.subject.keywordAuthor | Instruction sets | - |
dc.subject.keywordAuthor | Bayesian networks | - |
dc.subject.keywordAuthor | structure learning | - |
dc.subject.keywordAuthor | heuristic search algorithm | - |
dc.subject.keywordAuthor | parallel structure learning | - |
dc.subject.keywordAuthor | memoization | - |
dc.subject.keywordAuthor | simulated annealing with a greedy algorithm | - |
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