Evolution of Fuzzy Classifiers using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Muni:2012:FIE,

• author = "Durga Prasad Muni and Nikhil R. Pal",
• title = "Evolution of Fuzzy Classifiers using Genetic Programming",
• journal = "Fuzzy Information and Engineering",
• year = "2012",
• volume = "4",
• number = "1",
• pages = "29--49",
• month = mar,
• publisher = "Springer Berlin Heidelberg, in co-publication with Fuzzy Information and Engineering Branch of the Operations Research Society of China",
• keywords = "genetic algorithms, genetic programming, Fuzzy logic, Classification, Rule extraction, Evolutionary algorithms",
• ISSN = "1616-8658",
• DOI = "doi:10.1007/s12543-012-0099-8",
• size = "21 pages",
• abstract = "In this paper, we propose a genetic programming (GP) based approach to evolve fuzzy rule based classifiers. For a c-class problem, a classifier consists of c trees. Each tree, T_i , of the multi-tree classifier represents a set of rules for class i. During the evolutionary process, the inaccurate/inactive rules of the initial set of rules are removed by a cleaning scheme. This allows good rules to sustain and that eventually determines the number of rules. In the beginning, our GP scheme uses a randomly selected subset of features and then evolves the features to be used in each rule. The initial rules are constructed using prototypes, which are generated randomly as well as by the fuzzy k-means (FKM) algorithm. Besides, experiments are conducted in three different ways: Using only randomly generated rules, using a mixture of randomly generated rules and FKM prototype based rules, and with exclusively FKM prototype based rules. The performance of the classifiers is comparable irrespective of the type of initial rules. This emphasises the novelty of the proposed evolutionary scheme. In this context, we propose a new mutation operation to alter the rule parameters. The GP scheme optimises the structure of rules as well as the parameters involved. The method is validated on six benchmark data sets and the performance of the proposed scheme is found to be satisfactory.",
• affiliation = "Infosys Limited, Bangalore, India",

}

Genetic Programming entries for Durga Prasad Muni Nikhil Ranjan Pal

Citations