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Association-Based Evolution of Comprehensible Neural Logic Networks
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@MISC{Chia_association-basedevolution,
author = {Henry Wai-kit Chia and Chew-lim Tan},
title = {Association-Based Evolution of Comprehensible Neural Logic Networks},
year = {}
}
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Abstract
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-classifier. 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. 1
Keyphrases
association-based evolution comprehensible neural logic network general association-based classifier logic inference rule set evolved neulonets neural logic network accuracy-based fitness measure human comprehensibility single large neulonet eventual macro-classifier recent implementation logic expression inherent multiple compact neulonets empirical study present work associative classification complex human reasoning process fitness computation associative-classification measure
