A fixed structure learning automata‐based optimization algorithm for structure learning of Bayesian networks. Issue 7 (25th May 2021)

• Record Type: Journal Article
• Authors: Asghari, Kayvan; Masdari, Mohammad; Soleimanian Gharehchopogh, Farhad; Saneifard, Rahim
• Other Names: Stahl Frederic Theodor guestEditor.; Neagu Daniel guestEditor.; Campean Felician guestEditor.; Gheorghe Marian guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-25
• Subjects: Bayesian network -- learning automata -- score and search method -- structure learning; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12734
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem.