Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7606
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shum, Wing-Ho | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Wong, Man-Leung | en_US |
dc.date.accessioned | 2023-03-27T04:05:45Z | - |
dc.date.available | 2023-03-27T04:05:45Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Lecture Notes in Computer Science, 2005, Vol. 3578, pp. 546 - 554 | en_US |
dc.identifier.issn | 03029743 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7606 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Verlag | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.title | Co-evolutionary rule-chaining genetic programming | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | 10.1007/11508069_71 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
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