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A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Barros, Rodrigo C. and Basgalupp, Márcio P. and de Carvalho, André C.P.L.F. and Freitas, Alex A. (2012) A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms. In: Moore, Jason H. and Soule, Terence and Banzhaf, Wolfgang and Llora, Xavier and Auger, Anne and Ritchie, Marylyn and Ochoa, Gabriela and Rand, Bill and Bongard, Josh and Loiacono, Daniele and Mehnen, Joern and Davis, David, eds. Proceedings of the 14th annual conference on Genetic and evolutionary computation. ACM, New York, USA, pp. 1237-1244. ISBN 978-1-4503-1177-9. (doi:10.1145/2330163.2330335) (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:30803)

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.
Official URL:
http://dx.doi.org/10.1145/2330163.2330335

Abstract

Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

Item Type: Book section
DOI/Identification number: 10.1145/2330163.2330335
Uncontrolled keywords: data mining, hyper-heuristics, classification
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: Alex Freitas
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 16 Nov 2021 10:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/30803 (The current URI for this page, for reference purposes)

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