Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

Author Barros, Rodrigo C. Google Scholar
Basgalupp, Marcio P. Autor UNIFESP Google Scholar
Carvalho, Andre C. P. L. F. de Google Scholar
Freitas, Alex A. Google Scholar
Institution Universidade de São Paulo (USP)
Universidade Federal de São Paulo (UNIFESP)
Univ Kent
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.
Keywords Decision trees
automatic algorithm design
supervised machine learning
data mining
Language English
Date 2013-11-01
Published in Evolutionary Computation. Cambridge: Mit Press, v. 21, n. 4, p. 659-684, 2013.
ISSN 1063-6560 (Sherpa/Romeo, impact factor)
Publisher Mit Press
Extent 659-684
Access rights Closed access
Type Letter
Web of Science ID WOS:000326579700005

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