Learning decision trees in continuous space

Dombi József and Zsiros Ákos: Learning decision trees in continuous space. In: Acta cybernetica, (15) 2. pp. 213-224. (2001)

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Two problems of the ID3 and C4.5 decision tree building methods will be mentioned and solutions will be suggested on them. First, in both methods a Gain-type criteria is used to compare the applicability of possible tests, which derives from the entropy function. We are going to propose a new measure instead of the entropy function, which comes from the measure of fuzziness using a monotone fuzzy operator. It is more natural and much simpler to compute in case of concept learning (when elements belong to only two classes: positive and negative). Second, the well-known extension of the ID3 method for handling continuous attributes (C4.5) is based on discretization of attribute values and in it the decision space is separated with axis-parallel hyperplanes. In our proposed new method (CDT) continuous attributes are handled without discretization, and arbitrary geometric figures are used for separation of decision space, like hyperplanes in general position, spheres and ellipsoids. The power of our new method is going to be demonstrated oh a few examples.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2001
Volume: 15
Number: 2
ISSN: 0324-721X
Page Range: pp. 213-224
Language: English
Place of Publication: Szeged
Event Title: Conference for PhD Students in Computer Science (2.) (2000) (Szeged)
Related URLs: http://acta.bibl.u-szeged.hu/38512/
Uncontrolled Keywords: Számítástechnika, Kibernetika
Additional Information: Bibliogr.: 224. p. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.02. Computer and information sciences
Date Deposited: 2016. Oct. 15. 12:25
Last Modified: 2022. Jun. 14. 12:11
URI: http://acta.bibl.u-szeged.hu/id/eprint/12674

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