Continuous Endpoint Data Mining with ExSTraCS
Ryan J. Urbanowicz, Niranjan Ramanand, Jason H. Moore · 2015
ExSTraCS is a powerful Michigan-style learning classifier system (LCS) that was developed for classification, prediction, modeling, and knowledge discovery in complex and/or heterogeneous supervised learning problems with clean or noisy signals. To date, ExSTraCS has been limited to problems with discrete endpoints (i.e. classes). Many real world problems, however, involve endpoints with continuous values (e.g. function approximation, or quantitative trait analyses). In some problems the goal is to predict a specific continuous value with low error based on input values. In other problems it may be more informative to predict continuous intervals (i.e. predict that an endpoint falls within some range to define meaningful thresholds within the endpoint continuum). Thus far, there has not been a supervised learning LCS designed to handle continuous endpoints, nor one that incorporates interval predictions within rules. In this paper, we propose and evaluate (1) a supervised learning approach for solving continuous endpoint problems that connects input states to endpoint intervals within rules, (2) a novel prediction scheme that converts interval predictions into a specific continuous value prediction, and (3) an alternate approach to rule subsumption. Following simulation study analyses, we discuss the benefits and drawbacks of these implementations within ExSTraCS.