Decision tree search methods in fuzzy modeling and classification

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...

Full description

Saved in:
Bibliographic Details
Published in:International journal of approximate reasoning 2007-02, Vol.44 (2), p.106-123
Main Authors: Mendonça, L.F., Vieira, S.M., Sousa, J.M.C.
Format: Article
Language:English
Subjects:
Bottom-up approach
Decision trees
Fuzzy modeling
Input selection
Top-down approach
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2006.07.004