XCS is a new kind of learning classifier system that differs from the traditional one primarily in its definition of classifier fitness and its relation to contemporary reinforcement learning. Advantages of XCS include improved performance and an ability to form accurate maximal generalizations. This paper reviews recent research on XCS with respect to representation, predictive modeling, inter...

Recently, traditional data mining algorithms are challenged by two problems: streaming data, and changes in the hidden context. These challenges emerged from real-world applications such as network intrusion detection, credit card fraud detection, etc. Online or incremental learning becomes more important than ever for dealing with these problems. This chapter investigates XCS, an evolutionary ...

Wilson's XCS has rapidly become the most popular classier system of all time, and is a major focus of current research. XCS's primary distinguishing feature is that it bases rule tness on the accuracy with which rules predict reward, rather than the magnitude of the reward predicted (as traditional, strength-based systems do). XCS is a complex system and di ers from other systems in a number of...

In this study, we use the Extended Classifier System (XCS) to model the market behavior of financial time series, the purpose of which is to provide effective trading decision support. Several technical indicators and their firstand second-order derivatives are selected as the market descriptive variables, which are then used for XCS training. Then, the adaptive rules of the classifiers, which ...

XCS is a complex machine learning technique that combines credit apportionment techniques for rule evaluation with genetic algorithms for rule discovery to evolve a distributed set of sub-solutions online. Recent research on XCS has mainly focused on achieving a better understanding of the reinforcement component, yielding several improvements to the architecture. Nonetheless, studies on the ru...

Learning classifier system is a machine learning technique which combines genetic algorithm with the power of the reinforcement learning paradigm. This rule based system has been inspired by the general principle of Darwinian evolution and cognitive learning. XCS, eXtended Classifier System, is currently considered as state-of-the-art learning classifier systems due to its effectiveness in data...

We propose a co-adaptive approach to control coevolution-based eXtended Classifier System (XCS) parameters. Taking advantage of the on-line incremental learning capabilities of XCS, solutions that completely address target problems can be produced. A coevolution model allows two XCS systems to operate in parallel to solve target and parameter setting problems simultaneously. Since our approach ...

The evolutionary learning mechanism in XCS strongly depends on its accuracy-based fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy, fitness pressure most often also results in a pressure towards higher specificity. Moreover, fitness...

We analyze generalization with the XCS classi er system when the system is applied to animat problems in grid-worlds. The aim of the paper is to give an uni ed view of generalization with XCS, in order to explain some of the phenomena reported in the literature. Initially, we extend the results previously presented in the literature applying XCS to three environments. Our results con rm what al...

The accuracy-based fitness approach in XCS is one of the most significant changes in comparison with original learning classifier systems. Nonetheless, neither the scaled accuracy function, nor the importance of the relative fitness approach has been investigated in detail. The recent introduction of tournament selection to XCS has shown to make the system more independent from parameter settin...