文章基本信息

标题：FUZZY KERNEL K-MEDOIDS ALGORITHM FOR MULTICLASS MULTIDIMENSIONAL DATA CLASSIFICATION
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作者：ZUHERMAN RUSTAM ; AINI SURI TALITA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：80
期号：1
出版社：Journal of Theoretical and Applied
摘要：The success of the classification method is highly dependent on how to specify initial data as the initial prototype, dissimilarity functions that we used and the presence of outliers among the data. To overcome these obstacles, in this paper we present Fuzzy Kernel k-Medoids (FKkM) algorithm that we claim to be robust against outliers, invariant under translation and data transformation, as the combined development of Fuzzy LVQ, Fuzzy k-Medoids and Kernel Function. Based on the experiments, it provides a better accuracy than Support Vector Machines, Kernel Fisher Discriminant and RBF Neural Network for multiclass multidimensional data classification.
关键词：Classification; Fuzzy LVQ; Fuzzy K-Medoids; Kernel Function; Multiclass Multidimensional Data