A New Hybrid Architecture for the Discovery and Compaction of Knowledge: Breast Cancer Datasets Case Study

This paper reports on the development of a new hybrid architecture that integrates Learning Classifier Systems (LCS) with Rete-based production systems inference engine to improve the performance of the process of compacting LCS generated rules. While LCS is responsible for generating a complete ruleset from a given breast cancer pathological data set, an adapted Rete-based inference engine has been integrated for the efficient extraction of a minimal and representative ruleset from the original generated ruleset. This has resulted in an architecture that is hybrid, efficient, component-based, elegant, and extensible. Also, this has demonstrated significant savings in computing the match phase when building on the two main features of the Rete match algorithm, namely structural similarity and temporal redundancy. Finally, this architecture may be considered as a new platform for research on compaction of LCS rules using Rete-based inference engines.