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LEGAL-Tree: a lexicographic multi-objective genetic algorithm for decision tree induction

Basgalupp, Márcio P. and Barros, Rodrigo C. and de Carvalho, André C.P.L.F. and Freitas, Alex A. and Ruiz, Duncan D. (2009) LEGAL-Tree: a lexicographic multi-objective genetic algorithm for decision tree induction. In: Shin, S.Y. and Ossowski, S. and Martins, P. and Menezes, R. and Virol, M. and Hong, J. and Shin, D. and Palakal, M.J. and Fritzke, U. and Crosby, M. and Haddad, H.M., eds. SAC '09 Proceedings of the 2009 ACM symposium on Applied Computing. SAC Symposium on Applied Computing . ACM, New York, USA, pp. 1085-1090. ISBN 978-1-60558-166-8. (doi:10.1145/1529282.1529521) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:24075)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1145/1529282.1529521

Abstract

Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction algorithms have some limitations though, due to the typical strategy they implement: recursive top-down partitioning through a greedy split evaluation. This strategy is limiting in the sense that there is quality loss while the partitioning process occurs, creating statistically insignificant rules. In order to prevent the greedy strategy and to avoid converging to local optima, we present a novel Genetic Algorithm for decision tree induction based on a lexicographic multi-objective approach, and we compare it with the most well-known algorithm for decision tree induction, J48, over distinct public datasets. The results show the feasibility of using this technique as a means to avoid the previously described problems, reporting not only a comparable accuracy but also, importantly, a significantly simpler classification model in the employed datasets.

Item Type: Book section
DOI/Identification number: 10.1145/1529282.1529521
Uncontrolled keywords: data mining, classification, evolutionary algorithms, decision tree
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:13 UTC
Last Modified: 16 Feb 2021 12:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/24075 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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