Accuracy-based learning classifier systems: models, analysis and applications to classification tasks

  • Authors:

  • Ester Bernadó-Mansilla;Josep M. Garrell-Guiu

  • Affiliations:

  • Computing Science, Bell Laboratories, Lucent Technologies, 600-700, Mountain Avenue, Murray Hill, NJ and Enginyeria i Arquitectura La Salle, Ramor Llull University, Passeig Bonanova, 8. 08022 Barc ...;Enginyeria i Arquitectura La Salle, Ramon Llull University, Passeig Bonanova, 8. 08022, Barcelona, Spain

  • Venue:
  • Evolutionary Computation
  • Year:
  • 2003

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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.