Classification with fuzzy OWA distance
Fuzzy Theory and Its Applications(2014)
摘要
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.
更多查看译文
关键词
fuzzy set theory,pattern classification,cxk nearest neighbor algorithm,cxk-nn,k-nearest set,l-r fuzzy data,owa operator,fuzzy owa distance calculation,fuzzy metric measure,fuzzy point classification,intercluster distance approach,ordered weighted averaging,weighted dissimilarity measure,couplings,fuzzy sets,classification algorithms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要