A Survey on: Stratified mapping of Microarray Gene Expressiondatasets to decision tree algorithm aided through EvolutionaryDesign
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6
Abstract
Abstract: Analyzing gene expression data is a challenging task since the large number of features against theshortage of available examples can be prone to over fitting. In order to avoid this pitfall and achieve highperformance, some approaches construct complex classifiers, using new or well-established strategies. Inmedical decision making (classification, diagnosing) there are many situations where decision must be madeeffectively and reliably. Decision tree induction is one of the most employed methods to extract knowledge fromdata, since the representation of knowledge is very intuitive and easily understandable by humans. Conceptualsimple decision making models with the possibility of automatic learning are the most appropriate forperforming such tasks. Decision trees are a reliable and effective decision making technique that provide highclassification accuracy with a simple representation of gathered knowledge and they have been used in differentareas of medical decision making. Following recent breakthroughs in the automatic design of machine learningalgorithms, a novel approach of hyper-heuristic evolutionary algorithm called HEAD-DT that evolves designcomponents of top-down decision tree induction algorithms can be used to improve the classification accuracy
Authors and Affiliations
Ms. Neha V. Bhatambarekar , Prof. Payal S. Kulkarni
Keywords
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