title: Accuracy-based learning classifier systems: models, analysis and applications to classification tasks.
abstract: Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.
authors.affiliation: Computing Sciences, Bell Laboratories, Lucent Technologies, 600-700 Mountain Avenue, Murray Hill, NJ 07974-0636, USA. esterb@salleurl.edu
authors.forename: Ester
authors.initials: E
authors.lastname: Bernadó-Mansilla
authors.forename: Josep M
authors.initials: JM
authors.lastname: Garrell-Guiu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003663:Decision Trees; D006801:Humans; D007858:Learning; D008957:Models, Genetic; D015233:Models, Statistical