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JACIII Vol.2 No.4 pp. 128-133
doi: 10.20965/jaciii.1998.p0128
(1998)

Paper:

Generation of Fuzzy Rules from Decision Trees

Lawrence O. Hall and Petter Lande

Department of Computer Science and Engineering University of South Florida Tampa, Fl. 33620

Received:
November 24, 1997
Accepted:
March 26, 1998
Published:
August 20, 1998
Keywords:
Decision tree, Fuzzy, Continuous, Output, Function approximation
Abstract

The paper introduces two ways to develop fuzzy rules, using decision trees, from data with continuous valued inputs and outputs. A key problem is dealing with continuous outputs. Output classes are created, then a crisp decision tree is created using a set of fuzzy output classes and letting each training example to partially belong to classes. Alternatively, a discrete set of fuzzy outputs classes is created that includes a selected group of overlaps, such as class A.75/class B.25. Training examples are then provided to a standard decision tree learning program, such as C4.5. In both cases, fuzzy rules are extracted from the resulting decision tree. Output classes must be created for a case in which examples belong to discrete but overlapping classes. We discuss tradeoffs of the two approaches to output class creation. An example of system performance uses a discrete set of overlapping classes on the Box-Jenkins gas furnace prediction problem and a function approximation problem. The learned rules provide effective control and function approximation.

Cite this article as:
Lawrence O. Hall and Petter Lande, “Generation of Fuzzy Rules from Decision Trees,” J. Adv. Comput. Intell. Intell. Inform., Vol.2, No.4, pp. 128-133, 1998.
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