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

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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Pontificia Univ Catolica Rio Grande do Sul, Fac Informat, BR-90619900 Porto Alegre, RS, BrazilUniv São Paulo, Dept Comp Sci, BR-13566590 Sao Carlos, SP, BrazilUniversidade Federal de São Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos, BrazilUniv Kent, Dept Comp Sci, Canterbury CT2 7NF, Kent, EnglandUniversidade Federal de São Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos, BrazilFAPESP: 2009/14325-3Web of Scienc