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Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets

Barros, R.C., Basgalupp, M.P., Freitas, Alex A., de Carvalho, A.C.P.L.F. (2013) Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets. IEEE Transactions on Evolutionary Computation, 18 (6). pp. 873-892. ISSN 1089-778X. (doi:10.1109/TEVC.2013.2291813)

Abstract

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

Item Type: Article
DOI/Identification number: 10.1109/TEVC.2013.2291813
Uncontrolled keywords: data mining, machine learning, decision tree, classification, evolutionary algorithm, gene expression data, bioinformatics
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 10 Dec 2014 09:32 UTC
Last Modified: 04 Feb 2020 04:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/45928 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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