K-nearest neighbor (KNN) classifier

KNN is one of the most used classification model which is based on analog learning. Here in this method a comparison is done between the training and the testing tuples for finding the similarity. The input to the classifier consists of k-training samples in the feature set. The output of the KNN classifier is a type of class membership. The classification is done depending upon the majority of votes by its neighbors, with the object allocated to the most common neighbors. Here k is a small integer, and when k = 1, then the class is allocated to the class of a single nearest neighbor (Bramer, 2007). The basic structure of the KNN classifier is presented in Figure 13.10.