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

• Published in: IEEE transactions on evolutionary computation Vol. 18; no. 6; pp. 873 - 892
• Main Authors: Barros, Rodrigo C., Basgalupp, Marcio P., Freitas, Alex A., de Carvalho, Andre C. P. L. F.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithm design and analysis; Algorithms; automatic algorithm design; classification; Decision trees; Design; Evolution; Evolutionary algorithms; Evolutionary computation; Gene expression; hyperheuristics; Machine learning; Machine learning algorithms; Prediction algorithms; Strategy; Training; classification; machine learning; Automatic algorithm design; decision trees; evolutionary algorithms; hyper-heuristics
• ISSN: 1089-778X; 1941-0026; 1941-0026
• DOI: 10.1109/TEVC.2013.2291813

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.