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Nonparametric methods of supervised classification

Jóźwik A.

Journal of Medical Informatics & Technologies

2013

Vol. 22

21--32

Selected nonparametric methods of statistical pattern recognition are described. A part of them form modifications of the well known k-NN rule. To this group of the presented methods belong: a fuzzy k-NN rule, a pair-wise k-NN rule and a corrected k-NN rule. They can improve classification quality as compared with the standard k-NN rule. For the cases when these modifications would offer to large error rates an approach based on class areas determination is proposed. The idea of class areas can be also used for construction of the multistage classifier. A separate feature selection can be performed in each stage. The modifications of the k-NN rule and the methods based on determination class areas can be too slow in some applications, therefore algorithms for reference set reduction and condensation, for simple NN rule, are proposed. To construct fast classifiers it is worth to consider also a pair-wise linear classifiers. The presented idea can be used as in the case when the class pairs are linearly separable as well as in the contrary case.

Modyfikacja algorytmu Changa z wykorzystaniem metody znajdowania punktów najbliższych

Sierszeń A.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

2007

T. 11, z. 3

193-202

Modyfikacja algorytmu Changa polegająca na zastąpieniu oryginalnego sposobu wyznaczania odległości do najbliżej położonego punktu z tej samej klasy zmodyfikowanymi metodami znajdowania punktów najbliższych przynosi dużą akceleracje obliczeń. Przeprowadzone eksperymenty dowodzą, że zaproponowana metoda nie zmniejsza w sposób istotny jakości klasyfikacji.

The modification of Chang's algorithm consisting in replacement of the original method of determining the distance to the nearest point from the same class with modified methods of finding the mutually nearest points causes a great acceleration of the computational phase. Results of experiments show that the presented method does not significantly decrease the quality of classification.