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A novel Bayesian Belief Network structure learning algorithm based on bio-inspired monkey search meta heuristic

机译：基于生物启发式猴子搜索元启发式的贝叶斯信念网络结构学习算法

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Bayesian Belief Networks (BBN) combine available statistics and expert knowledge to provide a succinct representation of domain knowledge under uncertainty. Learning BBN structure from data is an NP hard problem due to enormity of search space. In recent past, heuristics based methods have simplified the search space to find optimal BBN structure (based on certain scores) in reasonable time. However, slow convergence and suboptimal solutions are common problems with these methods. In this paper, a novel searching algorithm based on bio-inspired monkey search meta-heuristic has been proposed. The jump, watch-jump and somersault sub processes are designed to give a global optimal solution with fast convergence. The proposed method, Monkey Search Structure Leaner (MS2L), is evaluated against five popular BBN structure learning approaches on model construction time and classification accuracy. The results obtained prove the superiority of our proposed algorithm on all metrics.
机译：贝叶斯信念网络（BBN）结合了可用的统计数据和专家知识，以在不确定性下提供领域知识的简洁表示。由于搜索空间巨大，从数据中学习BBN结构是一个NP难题。最近，基于启发式的方法简化了搜索空间，可以在合理的时间内找到最佳的BBN结构（基于某些分数）。但是，这些方法普遍存在收敛速度慢和解决方案不理想的问题。本文提出了一种新的基于生物启发式猴子搜索元启发式的搜索算法。跳转，监视和翻筋斗子过程旨在提供快速收敛的全局最佳解决方案。针对五种流行的BBN结构学习方法，针对模型构建时间和分类准确性，对提出的方法“猴子搜索结构学习者（MS2L）”进行了评估。获得的结果证明了我们提出的算法在所有指标上的优越性。

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