Data Mining Rules Using Multi-Objective Evolutionary Algorithms

de la Iglesia, B, Philpott, MS, Bagnall, AJ and Rayward-Smith, VJ (2003) Data Mining Rules Using Multi-Objective Evolutionary Algorithms. In: Proceedings of 2003 IEEE Congress on Evolutionary Computation, 2003-12-08 - 2003-12-12.

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Abstract

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 multi-objective 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.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 04 Jul 2011 09:31
Last Modified: 11 Apr 2019 15:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/23542
DOI: 10.1109/CEC.2003.1299857

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