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Analyzing gene expression data is a challenging task since the large number of features against the shortage of available examples can be prone to over fitting. In order to avoid this pitfall and achieve high performance, some approaches construct complex classifiers, using new or well-established strategies. In medical decision making (classification, diagnosing) there are many situations where decision must be made effectively and reliably. Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. Following recent breakthroughs in the automatic design of machine learning algorithms, a novel approach of hyper-heuristic evolutionary algorithm called HEAD-DT that evolves design components of top-down decision tree induction algorithms can be used to improve the classification accuracy.

A Survey on: Stratified mapping of Microarray Gene Expression datasets to decision tree algorithm aided through Evolutionary Design