Dependence modeling rule mining using multi-objective genetic algorithms
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Artigo de evento
Date
2008
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Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
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0
Authors
Oliveira G.M.B.D.
Takiguti M.C.S.
Martins L.G.A.
Takiguti M.C.S.
Martins L.G.A.
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Abstract
This work investigates the use of multi-objective genetic algorithms in the mining of accurate and interesting rules for the dependence modeling task. Dependence modeling is a generalization of the classification task in which a set of goal attributes is used. A multi-objective evolutionary environment named MO-miner was implemented based on the family of algorithms called non-dominated sorting genetic algorithms. Two desirable properties of the rules being mined - accuracy and interestingness - are simultaneously manipulated. MO-miner keeps the metrics related to these properties separated during the evolution, as different objectives used in the fitness calculus in a Pareto-based approach. The environment was applied to a public domain database named Nursery. The results obtained by MO-miner had been compared with those generated by a standard GA in order to identify the benefits related to the multi-objective approach.
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Keywords
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And genetic algorithms , Classification tasks , Dependence modeling , Interesting rules , Interestingness , Multi-objective , Multi-objective genetic algorithms , Non-dominated sorting genetic algorithms , Public domains , Rule minings