Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39762
Title: Confidence and support classification using genetically programmed neural logic networks
Authors: Chia, H.W.-K. 
Tan, C.-L. 
Issue Date: 2004
Source: Chia, H.W.-K.,Tan, C.-L. (2004). Confidence and support classification using genetically programmed neural logic networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3103 : 836-837. ScholarBank@NUS Repository.
Abstract: Typical learning classifier systems employ conjunctive logic rules for representing domain knowledge. The classifier XCS is an extension of LCS with the ability to learn boolean logic functions for data mining. However, most data mining problems cannot be expressed simply with boolean logic. Neural Logic Network (Neulonet) learning is a technique that emulates the complex human reasoning processes through the use of net rules. Each neulonet is analogous to a learning classifier that is rewarded using support and confidence measures which are often used in association-based classification. Empirical results shows promise in terms of generalization ability and the comprehensibility of rules. © Springer-Verlag Berlin Heidelberg 2004.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39762
ISSN: 03029743
Appears in Collections:Staff Publications

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