Novel One Prototype in Each of the Classes Embedded in Fuzzy K-nearest Neighbor
Year: 2025
Type: article
Abstract: Many researchers in the world are interested in classification algorithms. One of the most famous and popular algorithms is K-NN and Fuzzy K-NN. Nevertheless, the time complexity of both algorithms is. In our previously publication, we introduced the novel algorithm in which each of the classes has one representation. Although it is a good algorithm and helps to solve a time process problem but there are still problems with the accuracy rate of classification. Thus, we develop a novel one prototype in each of classes embedded in Fuzzy K-Nearest Neighbor to solve the problem for accuracy rate of classification. The system provides 99.55%, 98.25%, 83.43%, 69.07%, 80.01%, 100%, 96.25%, 81.81%, 80.65% and 83.27%% in MIT-CBCL, ORL, FEI, Georgia Tech, Pain Expression, JAFFE, Senthikumar, Yale, PICS and CMU AMP databases, respectively.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7083589635
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