Quarterly (spring, summer, fall, winter)
176 pp. per issue
7 x 10
ISSN
1063-6560
E-ISSN
1530-9304
2014 Impact factor:
2.37

Evolutionary Computation

Winter 2013, Vol. 21, No. 4, Pages 659-684
(doi: 10.1162/EVCO_a_00101)
© 2013 Massachusetts Institute of Technology
Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms
Article PDF (704.07 KB)
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.