Abstract
Machine learning (ML) algorithms have been capable of processing symbolic, categorial data only. Real-world problems, particularly in medicine, comprise not only symbolic, but also numerical attributes. There are several approaches to discretize (categorize) numerical attributes. This article describes two newer algorithms for such a discretization.
The first one has been designed and implemented in KEX (Knowledge Explorer) as its preprocessing procedure. The other discretization procedure was designed for the CN4 algorithm, a large extension of the well-known CN2. The discretization procedure in CN4 works on-line, i.e., it dynamically (within the induction) discretizes numerical attributes.
A large drawback of these discretization procedures, either off-line or on-line, is that they generate sharp bounds between intervals. One way how to eliminate an impurity around the interval borders is to fuzzify them. Here we introduce the newest empirical procedures for fuzzification, both off-line (within KEX) and on-line (CN4).
This chapter first surveys the methodology of empirical machine learning (Section 1), then attribute-based rile-inducing learning from examples (Section 2). Section 3 briefly introduces the KEX algorithm and Section 4 surveys CN4. The last Section focuses on discretization and fuzzification procedures, includes empirical results that compare performance of KEX, CN4, and other well-known machine learning algorithms as for discretization and fuzzification, and concludes with analysis.
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Bruha, I., Berka, P. (2000). Discretization and Fuzzification of Numerical Attributes in Attribute-Based Learning. In: Szczepaniak, P.S., Lisboa, P.J.G., Kacprzyk, J. (eds) Fuzzy Systems in Medicine. Studies in Fuzziness and Soft Computing, vol 41. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1859-8_6
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DOI: https://doi.org/10.1007/978-3-7908-1859-8_6
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