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Towards the automatic design of decision tree induction algorithms

Barros, Rodrigo C. and Basgalupp, Márcio P. and de Carvalho, André C.P.L.F. and Freitas, Alex A. (2011) Towards the automatic design of decision tree induction algorithms. In: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. ACM, New York, USA, pp. 182-196. ISBN 978-1-4503-0690-4. (doi:10.1145/2001858.2002050) (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:30747)

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/10.1145/2001858.2002050

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 two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user.

Item Type: Book section
DOI/Identification number: 10.1145/2001858.2002050
Uncontrolled keywords: determinacy analysis, Craig interpolants
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/30747 (The current URI for this page, for reference purposes)

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