NOVEL ONE PROTOTYPE IN EACH OF THE CLASSES EMBEDDED IN FUZZY K-NEAREST NEIGHBOR

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.