Fuzziness As A Recognition Problem:
Using Decision Tree Learning Algorithms
For Inducing Fuzzy Membership Functions
Author(s)
O. Nykänen
Abstract
In this article we establish a new method for inducing fuzzy set membership
degrees based on empirical training data. The approach is founded on the notion
of Redundant Decision Trees (RDT), a generalisation of regular crisp Decision
Trees (DT). RDTs suffice in capturing the attribute tests required for recognising
crisp concepts, from which the related fuzzy concepts may be unambiguously
derived. Potential applications of this method include categorisation and the
semiautomatic construction and the statistical evaluation of fuzzy concepts. In
addition, since the definition of the membership degrees is effectively based on a
robust DT machine learning algorithm, the induced fuzzy membership functions
generalise. Thus, with certain assumptions, they output sensible membership
degrees of previously unseen objects. In addition to introducing and analysing
the basic definitions and algorithms, we briefly evaluate their applicability with
examples and present some remarks concerning the scope of the approach.
Keywords: fuzzy sets, decision trees, machine learning, empirical categorisation.
1 Introduction
Many of the practical problems of implementing intelligent systems are related
to recognition and qualification problems. In short, the recognition problems
make it difficult to evaluate the state of the world, while the qualification
problems, partly because of the recognition problems, make it hard to define the
circumstances under which a given action is guaranteed to work [12].
These problems become concrete when implementing fuzzy systems based on
empirical data sets. By definition, fuzzy systems tolerate imprecision by
operating with fuzzy concepts (sets). Roughly speaking, there exists two
Keywords
fuzzy sets, decision trees, machine learning, empirical categorisation.
Related Book
Other papers in this volume
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