Session:

LBP - Late Breaking Papers

Title:

Association-Based Evolution of Comprehensible Neural Logic Networks

Authors:

Henry Wai-Kit Chia
Chew-Lim Tan

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.