Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks
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- @Article{10.1109/TKDE.2006.111,
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author = "Henry W. K. Chia and Chew Lim Tan and Sam Y. Sung",
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title = "Enhancing Knowledge Discovery via Association-Based
Evolution of Neural Logic Networks",
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journal = "IEEE Transactions on Knowledge and Data Engineering",
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volume = "18",
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number = "7",
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year = "2006",
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publisher = "IEEE Computer Society",
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address = "Los Alamitos, CA, USA",
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pages = "889--901",
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keywords = "genetic algorithms, genetic programming, Data mining,
knowledge acquisition, connectionism and neural nets,
rule-based knowledge representation",
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ISSN = "1041-4347",
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DOI = "doi:10.1109/TKDE.2006.111",
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abstract = "The comprehensibility aspect of rule discovery is of
emerging interest in the realm of knowledge discovery
in databases. Of the many cognitive and psychological
factors relating the comprehensibility of knowledge, we
focus on the use of human amenable concepts as a
representation language in expressing classification
rules. Existing work in neural logic networks (or
neulonets) provides impetus for our research; its
strength lies in its ability to learn and represent
complex human logic in decision-making using
symbolic-interpretable net rules. A novel technique is
developed for neulonet learning by composing net rules
using genetic programming. Coupled with a sequential
covering approach for generating a list of neulonets,
the straightforward extraction of human-like logic
rules from each neulonet provides an alternate
perspective to the greater extent of knowledge that can
potentially be expressed and discovered, while the
entire list of neulonets together constitute an
effective classifier. We show how the sequential
covering approach is analogous to association-based
classification, leading to the development of an
association-based neulonet classifier. Empirical study
shows that associative classification integrated with
the genetic construction of neulonets performs better
than general association-based classifiers in terms of
higher accuracies and smaller rule sets. This is due to
the richness in logic expression inherent in the
neulonet learning paradigm.",
- }
Genetic Programming entries for
Henry Wai-Kit Chia
Chew-Lim Tan
Sam Y Sung
Citations