Interpolation in the eXtended Classifier System: An architectural perspective

Abstract

Sparseness in the input space can be challenging for Learning Classifier Systems.Interpolation is proposed as a solution to tackle this problem.Two approaches to incorporate interpolation in the eXtended Classifier System are introduced.Experimental results promise beneficial effects during the learning process.The presented results improve the observations reported elsewhere. Machine Learning techniques constitute a key factor to make Organic Computing (OC) systems self-adaptive and self-reconfigurable at runtime. OC systems are therefore equipped with a so-called self-learning property enabling them to react appropriately when the environmental demands change and the system is faced possibly unforeseen situations. The eXtended Classifier System (XCS) is a rule-based evolutionary online learning system that has gained plenty attention in the research field of Genetic-based Machine Learning in general and within the OC initiative in particular. In this article, the XCS system is structurally extended to incorporate numerical interpolation. With the presented approaches we pursue the overall goal to overcome the challenge of sparsely distributed samples in the problem space resulting from e.g., non-uniform data distributions. A novel Interpolation Component (IC) is introduced and two architectural integration approaches are discussed. We elaborate on three strategies to integrate interpolated values into various algorithmic steps of XCS. The potential of incorporating interpolation techniques is underpinned by an evaluation on a rather challenging theoretical classification task, called the checkerboard problem.