%0 Conference Paper %1 conf/ideal/ShumLW05 %A Shum, Wing-Ho %A Leung, Kwong-Sak %A Wong, Man Leung %B Intelligent Data Engineering and Automated Learning - IDEAL 2005, 6th International Conference, Proceedings %C Brisbane, Australia %D 2005 %E Gallagher, Marcus %E Hogan, James M. %E Maire, Frédéric %I Springer %K Agents Complex Systems algorithms, and genetic programming, %P 546--554 %R doi:10.1007/11508069_71 %T Co-evolutionary Rule-Chaining Genetic Programming %V 3578 %X 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. %@ 3-540-26972-X

@inproceedings{conf/ideal/ShumLW05, abstract = {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.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Brisbane, Australia}, author = {Shum, Wing-Ho and Leung, Kwong-Sak and Wong, Man Leung}, bibdate = {2005-06-23}, bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/ideal/ideal2005.html#ShumLW05}, biburl = {https://www.bibsonomy.org/bibtex/2abd1f5d545a13a95e02706db78392283/brazovayeye}, booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2005, 6th International Conference, Proceedings}, doi = {doi:10.1007/11508069_71}, editor = {Gallagher, Marcus and Hogan, James M. and Maire, Fr{\'e}d{\'e}ric}, interhash = {5875b50e1e0468f322c72fe28d79677c}, intrahash = {abd1f5d545a13a95e02706db78392283}, isbn = {3-540-26972-X}, keywords = {Agents Complex Systems algorithms, and genetic programming,}, month = {July 6-8}, notes = {(1) Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong (2) Department of Information Systems, Lingnan University, Tuen Mun, Hong Kong}, pages = {546--554}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, size = {9 pages}, timestamp = {2008-06-19T17:51:38.000+0200}, title = {Co-evolutionary Rule-Chaining Genetic Programming}, volume = 3578, year = 2005 }