An Experimental Comparison of Three Probabilistic Approximations Used for Rule Induction

• Autorzy: Clark P. G. , Grzymala-Busse J.W.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present results of experiments on 166 incomplete data sets using three probabilistic approximations: lower, middle, and upper. Two interpretations of missing attribute values were used: lost and “do not care” conditions. Our main objective was to select the best combination of an approximation and a missing attribute interpretation. We conclude that the best approach depends on the data set. The additional objective of our research was to study the average number of distinct probabilities associated with characteristic sets for all concepts of the data set. This number is much larger for data sets with “do not care” conditions than with data sets with lost values. Therefore, for data sets with “do not care” conditions the number of probabilistic approximations is also larger.

Słowa kluczowe

EN

characteristic set; incomplete data; lower; middle and upper approximations; singleton; subset and concept approximations

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 177--191
• Opis fizyczny: Bibliogr. 20 poz., tab., wykr.

Twórcy

autor

Clark P. G.

• Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA.

autor

Grzymala-Busse J.W.

• Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
• Institute of Computer Science, Polish Academy of Sciences, 01-237 Warsaw, Poland

Bibliografia

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