Developments on a Multi-objective Metaheuristic (MOMH) Algorithm for Finding Interesting Sets of Classification Rules

de la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826, Reynolds, Alan and Rayward-Smith, Vic J. (2005) Developments on a Multi-objective Metaheuristic (MOMH) Algorithm for Finding Interesting Sets of Classification Rules. In: Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, 3410 . Springer Berlin / Heidelberg, pp. 826-840. ISBN 978-3-540-24983-2

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Abstract

In this paper, we experiment with a combination of innovative approaches to rule induction to encourage the production of interesting sets of classification rules. These include multi-objective metaheuristics to induce the rules; measures of rule dissimilarity to encourage the production of dissimilar rules; and rule clustering algorithms to evaluate the results obtained. Our previous implementation of NSGA-II for rule induction produces a set of cc-optimal rules (coverage-confidence optimal rules). Among the set of rules produced there may be rules that are very similar. We explore the concept of rule similarity and experiment with a number of modifications of the crowding distance to increasing the diversity of the partial classification rules produced by the multi-objective algorithm.

Item Type:	Book Section
Additional Information:	Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005
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 Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Vishal Gautam
Date Deposited:	14 Jun 2011 15:41
Last Modified:	18 Apr 2023 01:01
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23538
DOI:	10.1007/978-3-540-31880-4_57

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