Enhancing knowledge discovery via association-based evolution of neural logic networks

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.