Predictive and comprehensible rule discovery using a multi-objective genetic algorithm |
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| Affiliation: | 1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, Pietermaritzburg, South Africa;2. Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain;1. Laboratory LAR-GHYDE, University of Biskra, Algeria;2. Faculty of Eng. &Tech., Chem. Eng. Dept. The University of Jordan 11942-Amman, Jordan;3. Laboratory of engineering sciences for environment (LaSIE), University of La Rochelle, France;4. Chair of Separation Science and Technology, Center for Mathematical Modeling, Kaiserslautern University, P.O. Box 3049, D-67653 Kaiserslautern, Germany |
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| Abstract: | We present a multi-objective genetic algorithm for mining highly predictive and comprehensible classification rules from large databases. We emphasize predictive accuracy and comprehensibility of the rules. However, accuracy and comprehensibility of the rules often conflict with each other. This makes it an optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm (INPGA) for this purpose. We have compared the rule generation by INPGA with that by simple genetic algorithm (SGA) and basic niched Pareto genetic algorithm (NPGA). The experimental result confirms that our rule generation has a clear edge over SGA and NPGA. |
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