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

Abstract

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.