Bayes classification of imprecise information of interval type

• Autorzy: Kulczycki P. , Kowalski P. A.
• Języki publikacji: EN

Abstrakty

EN

The subject of the investigation presented here is Bayes classification of imprecise multidimensional information of interval type by means of patterns defined through precise data, e.g. deterministic or sharp. For this purpose the statistical kernel estimators methodology was applied, which makes the resulting algorithm independent of the pattern shape. In addition, elements of pattern sets which have insignificant or negative influence on the correctness of classification are eliminated. The concept for realizing the procedure is based on the sensitivity method, used in the domain of artificial neural networks. As a result of this procedure the number of correct classifications and - above all - calculation speed increased significantly. A further growth in quality of classification was achieved with an algorithm for the correction of classifier parameter values. The results of numerical verification, carried out on pseudorandom and benchmark data, as well as a comparative analysis with other methods of similar conditioning, have validated the concept presented here and its positive features.

Słowa kluczowe

EN

data analysis; classification; imprecise information; interval type information; statistical kernel estimators; reduction in pattern size; classifier parameter correction; sensitivity method for artificial neural networks

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2011
• Tom: Vol. 40, no 1
• Strony: 101--123
• Opis fizyczny: Bibliogr. 32 poz., wykr.

Twórcy

autor

Kulczycki P.

autor

Kowalski P. A.

• Systems Research Institute, Polish Academy of Sciences Newelska 6, 01-447 Warszawa, Poland,

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