Generate classifier for Genetic Programming of Multicategory Pattern Classification Using Multiclass Microarray Datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@Article{Mansuri:2013:ijarcs,

• author = "Anwar Mohd Mansuri and Deepali Kelkar",
• title = "Generate classifier for Genetic Programming of Multicategory Pattern Classification Using Multiclass Microarray Datasets",
• journal = "International Journal of Advanced Research in Computer Science",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, microarray, classifier, mutation, crossover",
• ISSN = "0976-5697",
• bibsource = "OAI-PMH server at www.doaj.org",
• oai = "oai:doaj-articles:8fe02df864e6a4d6368faf194ea13abd",
• URL = "http://www.ijarcs.info/Mansuri:2013:ijarcs.pdf",
• size = "4 pages",
• 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.",

}

Genetic Programming entries for Anwar Mohd Mansuri Deepali Kelkar

Citations