Dynamic BN and Multi-Objective GA
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
NERA02_086
تاریخ نمایه سازی: 7 اسفند 1396
Abstract:
Bayesian Network (BN) is a probabilistic approach for reasoning under uncertainty, and has become a popular knowledge representation scheme in several fields such as data mining and knowledge discovery. A BN is a graphical model which denotes a joint probabilistic distribution of given variables under their dependence relationships. In this paper, We use a multiobjective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network’s probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promisingresults when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA.
Keywords:
Genetic Algorithms (GA) , Directed Acyclic Graph(DAG) , Dynamic Bayesian Network (DBN) , Bayesian Information Criterion (BIC) , Minimal Description Length (MDL)
Authors
Ali Asghar Mohammadi
Nima Institute of Higher Education-Mahmudabad Amol, Iran
Hassan Saeidi
Department of computer engineering Faculty of mahmoudabad Technical and vocational university(TVU), Tehran, Iran