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Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

Barros, Rodrigo C., Basgalupp, Marcio P., Freitas, Alex A., de Carvalho, Andre C.P.L.F. (2013) Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms. Evolutionary Computation, 21 (4). pp. 659-684. ISSN 1063-6560. (doi:10.1162/EVCO_a_00101) (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)

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/10.1162/EVCO_a_00101

Abstract

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

Item Type: Article
DOI/Identification number: 10.1162/EVCO_a_00101
Uncontrolled keywords: evolutionary algorithm, classification, data mining, machine learning, decision tree
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 06 Dec 2013 17:53 UTC
Last Modified: 29 May 2019 11:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37252 (The current URI for this page, for reference purposes)
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