Association-Based Evolution of Comprehensible Neural Logic Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{chia:2004:lbp,
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author = "Henry Wai-Kit Chia and Chew-Lim Tan",
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title = "Association-Based Evolution of Comprehensible Neural
Logic Networks",
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booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
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year = "2004",
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editor = "Maarten Keijzer",
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address = "Seattle, Washington, USA",
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month = "26 " # jul,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP061.pdf",
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abstract = "Neural Logic Network (Neulonet) learning has been
successfully used in emulating complex human reasoning
processes. One recent implementation generates a single
large neulonet via genetic programming using an
accuracy-based fitness measure. However, in terms of
human comprehensibility and amenability during logic
inference, evolving multiple compact neulonets are
preferred. The present work realizes this by adopting
associative-classification measures of confidence and
support as part of the fitness computation. The evolved
neulonets are combined together to form an eventual
macro-classier. Empirical study shows that associative
classification integrated with neulonet learning
performs better than general association-based
classifiers in terms of higher accuracies and smaller
rule sets. This is primarily due to the richness in
logic expression inherent in the neulonet learning
paradigm.",
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notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
- }
Genetic Programming entries for
Henry Wai-Kit Chia
Chew-Lim Tan
Citations