Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7606
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dc.contributor.authorShum, Wing-Hoen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong, Man-Leungen_US
dc.date.accessioned2023-03-27T04:05:45Z-
dc.date.available2023-03-27T04:05:45Z-
dc.date.issued2005-
dc.identifier.citationLecture Notes in Computer Science, 2005, Vol. 3578, pp. 546 - 554en_US
dc.identifier.issn03029743-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7606-
dc.description.abstractA 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.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.titleCo-evolutionary rule-chaining genetic programmingen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1007/11508069_71-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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