Decision tree search methods in fuzzy modeling and classification

  • Authors:

  • L. F. Mendonça;S. M. Vieira;J. M. C. Sousa

  • Affiliations:

  • Technical University of Lisbon, Instituto Superior Técnico, Department of Mechanical Engineering, GCAR/IDMEC, Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal and Esco ...;Technical University of Lisbon, Instituto Superior Técnico, Department of Mechanical Engineering, GCAR/IDMEC, Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal;Technical University of Lisbon, Instituto Superior Técnico, Department of Mechanical Engineering, GCAR/IDMEC, Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2007

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Abstract

This paper proposes input selection methods for fuzzy modeling, which are based on decision tree search approaches. The branching decision at each node of the tree is made based on the accuracy of the model available at the node. We propose two different approaches of decision tree search algorithms: bottom-up and top-down and four different measures for selecting the most appropriate set of inputs at every branching node (or decision node). Both decision tree approaches are tested using real-world application examples. These methods are applied to fuzzy modeling of two different classification problems and to fuzzy modeling of two dynamic processes. The models accuracy of the four different examples are compared in terms of several performance measures. Moreover, the advantages and drawbacks of using bottom-up or top-down approaches are discussed.