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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