Modified K-Nearest Neighbor Classifier Using Group Prototypes and its Application to Fault Diagnosis

This paper describes, proposed modified K-NN (MKNN) classifier, which calculates group prototypes from several patterns belonging to the same class and uses these prototypes for the recognition of patterns. Number of prototypes created by MKNN classifier is dependant on the distance factor d. More prototypes are created for smaller value of d and vice versa. We have compared performance of original KNN and MKNN using a fault diagnosis databases. From the experimentation, one can conclude that performance of MKNN is better than original KNN, in terms of percentage recognition rate and recall time per pattern, classification and classification time. MKNN, thus has increased the scope of original KNN for its application to large data sets, which was not possible previously.