Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 32, Issue 6, Pages 1157-1166Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2899096
Keywords
Simulated annealing; Markov processes; Greedy algorithms; Bayes methods; Search problems; Convergence; Instruction sets; Bayesian networks; structure learning; heuristic search algorithm; parallel structure learning; memoization; simulated annealing with a greedy algorithm
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Funding
- Samsung Electronics, Co., Ltd.
- Brain Korea PLUS
- Basic Science Research Program through the National Research Foundation of Korea - Ministry of Science, ICT, and Future Planning [NRF-2016R1A2B1008994]
- Ministry of Trade, Industry & Energy under the Industrial Technology Innovation Program [R1623371]
- National Research Foundation of Korea [22A20130012646] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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