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Lughofer and Buchtala proposed the idea of all-pairs evolving fuzzy classifiers for multi-class classification. For each pair of classes, a binary classifier is used to classify all the training samples belonging to these classes. Two fuzzy classification architectures, singleton class labels and regression-based classifiers based on Takagi-Sugeno (T-S) models, are used as binary classifiers. The...
In this paper, we give a comparison of four methods for solving clustering problems, including similarity-based fuzzy clustering (SFC), elliptic basis function (EBF), versatile elliptic basis function (VEBF), and similarity-based fuzzy clustering with principal component analysis (PCSFC). PCSFC is a modified version of SFC with rotation, while VEBF is a refined version of EBF. SFC and PCSFC are based...
Data mining is used widely to mine hidden knowledge and information from huge data. Classification is an important task in data mining, and it has been successfully applied in various fields. We propose a multi-class classification method, Adaptive Distance-Based Voting Classification (ADVC), based on voting on the distances of the global training samples with adaptive and practical voting thresholds...
Multi-valued Neuron with Periodic activation function (MVN-P) was proposed for solving classification problems. The boundaries between two distinct categories are precisely specified in MVN-P, which may cause slow convergence in learning or low classification accuracy in generalization. In this paper, we propose a revised model, MVN-PFT, in which a fuzzy tolerating buffer is provided around a boundary...
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