Data Mining Rules Using Multi-Objective Evolutionary Algorithms
نویسندگان
چکیده
In data mining, nugget discovery is the discovery of interesting classification rules that apply to a target class. In previous research, heuristic methods (Genetic algorithms, Simulated Annealing and Tabu Search) have been used to optimise a single measure of interest. This paper proposes the use of multiobjective optimisation evolutionary algorithms to allow the user to interactively select a number of interest measures and deliver the best nuggets (an approximation to the Pareto-optimal set) according to those measures. Initial experiments are conducted on a number of databases, using an implementation of the Fast Elitist Non-Dominated Sorting Genetic Algorithm (NSGA), and two well known measures of interest. Comparisons with the results obtained using modern heuristic methods are presented. Results indicate the potential of multi-objective evolutionary algorithms for the task of nugget discovery.
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