A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Three-cornered coevolution learning classifier systems for classification tasks

The Three-Cornered Coevolution concept describes a framework where artificial problems may be generated in concert with classification agents in order to provide insight into their relationships. This is unlike standard studies where humans set a problem's difficulty, which may have bias or lack understanding of the multiple interactions of a problem's characteristics, such as noise in conjunction with class imbalance. Previous studies have shown that it is feasible to generate problems with one agent in relation to a single classification agent's performance, but when to adjust the problem difficulty was manually set. This paper introduces a second classification agent to trigger the coevolutionary process within the system, where its functionality and effect on the system requires investigation. The classification agents, in this case Learning Classifier Systems, use different styles of learning techniques (e.g. supervised or reinforcement learning techniques) to learn the problems. Experiments show that the realised system is capable of autonomously generating various problems, triggering learning and providing insight into each learning system's ability by determining the problem domains where they perform relatively well - this is in contrast to humans having to determine the problem domains.</p

Facetwise Analysis of XCS for Problems With Class Imbalances

Michigan-style learning classifier systems (LCSs) are online machine learning techniques that incrementally evolve distributed subsolutions which individually solve a portion of the problem space. As in many machine learning systems, extracting accurate models from problems with class imbalances-that is, problems in which one of the classes is poorly represented with respect to the other classes-has been identified as a key challenge to LCSs. Empirical studies have shown that Michigan-style LCSs fail to provide accurate subsolutions that represent the minority class in domains with moderate and large disproportion of examples per class; however, the causes of this failure have not been analyzed in detail. Therefore, the aim of this paper is to carefully examine the effect of class imbalances on different LCS components. The analysis focuses on XCS, which is the most-relevant Michigan-style LCS, although the models could be easily adapted to other LCSs. Design decomposition is used to identify five elements that are crucial to guaranteeing the success of LCSs in domains with class imbalances, and facetwise models that explain these different elements for XCS are developed. All theoretical models are validated with artificial problems. The integration of all these models enables us to identify the sweet spot where XCS is able to scalably and efficiently evolve accurate models of rare classes; furthermore, facetwise analysis is used as a tool for designing a set of configuration guidelines that have to be followed to ensure convergence. When properly configured, XCS is shown to be able to solve highly unbalanced problems that previously eluded solution