Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7606
Title: Co-evolutionary rule-chaining genetic programming
Authors: Shum, Wing-Ho 
Prof. LEUNG Kwong Sak 
Wong, Man-Leung 
Issue Date: 2005
Publisher: Springer Verlag
Source: Lecture Notes in Computer Science, 2005, Vol. 3578, pp. 546 - 554
Journal: Lecture Notes in Computer Science 
Abstract: A novel Genetic Programming (GP) paradigm called Co-evolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes represented by a set of classification rules for multi-class problems. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. Its main advantages are: 1) it can handle more than one class at a time; 2) it avoids cyclic result; 3) unlike Bayesian Network (BN), the CRGP can handle input attributes with continuous values directly; and 4) with the flexibility of GP, CRGP can learn complex relationship. We have demonstrated its better performance on one synthetic and one real-life medical data sets. © Springer-Verlag Berlin Heidelberg 2005.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/7606
ISSN: 03029743
DOI: 10.1007/11508069_71
Appears in Collections:Applied Data Science - Publication

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