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Toolsde la Iglesia, B., Philpott, M. S., Bagnall, A. J. and Rayward-Smith, V. J. (2003) Data Mining Rules Using Multi-Objective Evolutionary Algorithms. In: 2003 IEEE Congress on Evolutionary Computation, 2003-12-08 - 2003-12-12.
Full text not available from this repository. (Request a copy)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 |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 04 Jul 2011 08:31 |
| Last Modified: | 28 Mar 2025 13:24 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/23542 |
| DOI: | 10.1109/CEC.2003.1299857 |
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