DocumentCode :
234696
Title :
A novel Bayesian Belief Network structure learning algorithm based on bio-inspired monkey search meta heuristic
Author :
Mittal, Sparsh ; Gopal, Kartik ; Maskara, S.L.
Author_Institution :
Deptt. of CS&IT, Jaypee Inst. of Inf. Technol., Noida, India
fYear :
2014
fDate :
7-9 Aug. 2014
Firstpage :
141
Lastpage :
147
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.
Keywords :
Bayes methods; belief networks; computational complexity; convergence; heuristic programming; learning (artificial intelligence); pattern classification; search problems; BBN structure learning; Bayesian belief network structure learning algorithm; MS2L; NP hard problem; bio-inspired monkey search meta heuristic; classification accuracy; convergence; domain knowledge representation; expert knowledge; heuristics based methods; model construction time; monkey search structure leaner; search space; searching algorithm; somersault subprocess; statistics; suboptimal solution; watch-jump subprocess; Approximation algorithms; Bayes methods; Diseases; Heuristic algorithms; Inference algorithms; Linear programming; Vegetation; Bayesian Belief Networks; Monkey Search; Probabilistic Classifiers; Structure Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Contemporary Computing (IC3), 2014 Seventh International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-5172-7
Type :
conf
DOI :
10.1109/IC3.2014.6897163
Filename :
6897163
Link To Document :
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