Learning discriminant functions with fuzzy attributes for classification using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Chien:2002:ESA,

• author = "Been-Chian Chien and Jung Yi Lin and Tzung-Pei Hong",
• title = "Learning discriminant functions with fuzzy attributes for classification using genetic programming",
• journal = "Expert Systems with Applications",
• year = "2002",
• volume = "23",
• pages = "31--37",
• number = "1",
• owner = "wlangdon",
• keywords = "genetic algorithms, genetic programming, Classification, Knowledge discovery, Fuzzy sets",
• ISSN = "0957-4174",
• URL = "http://www.sciencedirect.com/science/article/B6V03-45C00T2-1/2/e7d49cc18dd12961ac2e5c114c41f667",
• DOI = "doi:10.1016/S0957-4174(02)00025-8",
• abstract = "Classification is one of the important tasks in developing expert systems. Most of the previous approaches for classification problem are based on classification rules generated by decision trees. we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental learning strategy and a distance-based fitness function are developed to improve the efficiency of genetic programming-based learning process. We first transform attributes of objects into fuzzy attributes and then a set of discriminant functions is generated based on the proposed learning procedure. The set of derived functions with fuzzy attributes gives high accuracy of classification and presents a linear form. Hence, the functions can be transformed into inference rules easily and we can use the rules to provide the building of rule base in an expert system.",

}

Genetic Programming entries for Been-Chian Chien Mick Jung-Yi Lin Tzung-Pei Hong

Citations