Title :
A fuzzy K-NN algorithm using weights from the variance of membership values
Author :
Han, Joon H. ; Kim, Yoon K.
Author_Institution :
Dept. of Comput. Sci. & Eng., POSTECH, Pohang, South Korea
Abstract :
In this paper, a new fuzzy K-nearest neighbor (K-NN) algorithm, called “Variance Weighted Fuzzy K-NN”, is proposed. The main idea of this method is in giving weights to neighbors according to the standard deviation of their class membership values which reflect the value of a discriminant function. The classification results of 32 classes of complex images are given. Compared to the K-NN and fuzzy K-NN algorithms, our method shows an improved classification rate for various conditions
Keywords :
computational geometry; computer vision; fuzzy logic; image classification; classification rate; classification results; fuzzy K-NN algorithm; fuzzy K-nearest neighbor algorithm; membership values variance; standard deviation; variance weighted fuzzy K-NN; weights; Computer science; Equations; Marine vehicles; Nearest neighbor searches; Neural networks; Pattern classification; Pattern recognition; Telecommunications;
Conference_Titel :
Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Conference_Location :
Fort Collins, CO
Print_ISBN :
0-7695-0149-4
DOI :
10.1109/CVPR.1999.784711
