Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System

Michigan-style learning classifier systems have availed themselves as a promising modeling and data mining strategy for bioinformaticists seeking to connect predictive variables with disease phenotypes. The resulting ‘model’ learned by these algorithms is comprised of an entire population of rules, some of which will inevitably be redundant or poor predictors. Rule compaction is a post-processing strategy for consolidating this rule population with the goal of improving interpretation and knowledge discovery. However, existing rule compaction strategies tend to reduce overall rule population performance along with population size, especially in the context of noisy problem domains such as bioinformatics. In the present study we introduce and evaluate two new rule compaction strategies (QRC, PDRC) and a simple rule filtering method (QRF), and compare them to three existing methodologies. These new strategies are tuned to fit with a global approach to knowledge discovery in which less emphasis is placed on minimizing rule population size (to facilitate manual rule inspection) and more is placed on preserving performance. This work identified the strengths and weaknesses of each approach, suggesting PDRC to be the most balanced approach trading a minimal loss in testing accuracy for significant gains or consistency in all other performance statistics.