An Extension of Fuzzy L-R Data Classification with Fuzzy OWA Distance

K-Nearest neighbor (K-NN) algorithm is a classification algorithm widely used in machine learning, statistical pattern recognition, data mining, etc. Ordered weighted averaging (OWA) distance based CxK nearest neighbor algorithm is a kind of K-NN algorithm based on OWA distance. In this study, the aim is two-fold: i) to perform the algorithm with two different fuzzy metric measures, which are Diamond distance, and weighted dissimilarity measure composed by spread distances and center distances, and ii) to evaluate the effects of different metric measures. K neighbors are searched for each class, and OWA distance is used to aggregate the information. The OWA distance can behave as intercluster distance approaches single, complete, and average linkages by using different weights. The experimental study is performed on well-known three classification data sets (iris, glass, and wine). N-fold cross-validation is used for the evaluation of performances. It is seen that single linkage approach by using two different metric measures has significant different results.