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.
|Title of host publication||Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005 : proceedings|
|Number of pages||9|
|Publication status||Published - 1 Jan 2005|
Bibliographical notePaper presented at the 6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), 6-8 July 2005, Brisbane, Australia.
ISBN of the source publication: 9783540269724
SHUM, W. H., LEUNG, K. S., & WONG, M. L. (2005). Co-evolutionary rule-chaining genetic programming. In Intelligent Data Engineering and Automated Learning, IDEAL 2005, 6th International Conference, Brisbane, Australia, July 6-8, 2005 : proceedings (pp. 546-554). Springer. https://doi.org/10.1007/11508069_71