Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@Article{Barros:2014:ieeeTEC,

• author = "Rodrigo C. Barros and Marcio P. Basgalupp and Alex A. Freitas and Andre C. P. L. F. {de Carvalho}",
• title = "Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2014",
• month = dec,
• volume = "18",
• number = "6",
• pages = "873--892",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1089-778X",
• DOI = "10.1109/TEVC.2013.2291813",
• size = "20 pages",
• 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.",
• notes = "also known as \cite{6670778}",

}

Genetic Programming entries for Rodrigo C Barros Marcio Porto Basgalupp Alex Alves Freitas Andre Ponce de Leon F de Carvalho

Citations