Generation of Fuzzy Rules from Decision Trees
Document Type
Article
Publication Date
1998
Keywords
Decision tree, Fuzzy, Continuous, Output, Function approximation
Digital Object Identifier (DOI)
https://doi.org/10.20965/jaciii.1998.p0128
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.
Rights Information
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Journal of Advanced Computational Intelligence and Intelligent Informatics, v. 2, issue 4, p. 128-133
Scholar Commons Citation
Hall, Lawrence O. and Lande, Petter, "Generation of Fuzzy Rules from Decision Trees" (1998). Computer Science and Engineering Faculty Publications. 133.
https://digitalcommons.usf.edu/esb_facpub/133
