Speeding-up pittsburgh learning classifier systems: Modeling time and accuracy

Abstract

Windowing methods are useful techniques to reduce the computational cost of Pittsburgh-style genetic-based machine learning techniques. If used properly, they additionally can be used to improve the classification accuracy of the system. In this paper we develop a theoretical framework for a windowing scheme called ILAS, developed previously by the authors. The framework allows us to approximate the degree of windowing we can apply to a given dataset as well as the gain in runtime. The framework sets the first stage for the development of a larger methodology with several types of learning strategies in which we can apply ILAS, such as maximizing the learning performance of the system, or achieving the maximum run-time reduction without significant accuracy loss. © Springer-Verlae 004.