Classification with fuzzy OWA distance
Conference paper > Full Text · 2014
Authors: Gözde ULUTAGAY, Suzan Kantarci
2014 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2014, United States of America, Kaohsiung, International, Invitee, Full Text, 2014-2014, pp 195-198
DOI: 10.1109/iFUZZY.2014.7091258
Summary
OWA (Ordered Weighted Averaging) Distance Based CxK Nearest Neighbor Algorithm (CxK-NN) via L-R fuzzy data is performed with two different fuzzy metric measures. We use fuzzy metric defined by Diamond and a weighted dissimilarity measure composed by spread distances and center distances in order to evaluate the effects of different metric measures. K neighbors are considered for each class and the algorithm perform OWA operator in order to calculate the distance between being classified fuzzy point and its K-nearest set. It is observed that the OWA distance behavior by changing its weights as inter-cluster distance approaches single, complete, and average linkages. The performance of this novel approach is evaluated by using nfold cross validation. After experiments with well-known three classification dataset, it is observed that single linkage approach by using two different metric measures has significant different results.