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, multi-objective, 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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