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.
|Number of pages||5|
|Journal||Journal of Theoretical and Applied Information Technology|
|Publication status||Published - 1 Jan 2015|
- Fuzzy K-medoids
- Fuzzy LVQ
- Kernel function
- Multiclass multidimensional data