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Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning
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DOI：10.1109/TKDE.2019.2899096.png)
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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.
Keyword:
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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分享收藏A parallel algorithm for Bayesian network structure learning from large data sets一种面向大数据集的贝叶斯网络结构学习并行算法
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