Dependence Modeling Rule Mining using Multi-Objective Genetic Algorithms

G.M. Barbosa de Oliveira, M.C.S. Takiguti, and L. Gustavo Almeida Martins (Brazil)


Data mining, dependence modeling, multiobjective, artificial intelligence, and genetic algorithms.


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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