Uncertainty measures of rough set prediction

Autor(en): Duntsch, I
Gediga, G
Stichwörter: attribute prediction; Computer Science; Computer Science, Artificial Intelligence; minimum description length principle; rough set model
Erscheinungsdatum: 1998
Herausgeber: ELSEVIER SCIENCE BV
Journal: ARTIFICIAL INTELLIGENCE
Volumen: 106
Ausgabe: 1
Startseite: 109
Seitenende: 137
Zusammenfassung: 
The main statistics used in rough set data analysis, the approximation quality, is of limited value when there is a choice of competing models for predicting a decision variable. In keeping within the rough set philosophy of non-invasive data analysis, we present three model selection criteria, using information theoretic entropy in the spirit of the minimum description length principle. Our main procedure is based on the principle of indifference combined with the maximum entropy principle, thus keeping external model assumptions to a minimum. The applicability of the proposed method is demonstrated by a comparison of its error rates with results of C4.5, using 14 published data sets. (C) 1998 Elsevier Science B.V. All rights reserved.
ISSN: 00043702
DOI: 10.1016/S0004-3702(98)00091-5

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