Generate classifier for Genetic Programming of Multicategory Pattern Classification Using Multiclass Microarray Datasets
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Abstract
In this paper a multiclass classification problem solving technique based on genetic programming is presented. This paper
explores the feasibility of applying genetic programming (GP) to multicategory pattern classification.GP can discover relationships
among observed data and express them mathematically Feature selection approaches have been widely applied to deal with the small
sample size problem in the analysis of microarray datasets. Multiclass problem, the proposed methods are based on the idea of
selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem by splitting it into a
set of two- class problems and solving each problem with a respective classification system, Data mining deals with the problem of
discovering novel and interesting knowledge from large amount of data. The results obtained show that by applying Modified
crossover together with Point Mutation improves the performance of the classifier. A comparison with the results achieved by other
techniques on a classical benchmark set is carried out.
Keywords: Microarray; Classifier; Genetic Programming; Mutation, Crossover
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