Text classification using similarity measures on intuitionistic fuzzy sets
نویسنده
چکیده
An intuitionistic fuzzy set (IFS) is an extended version of a fuzzy set and is capable of representing hesitancy degrees. A framework for text classification is presented. Two main challenges are addressed: how to represent documents in terms of IFSs and how to obtain a pattern of each class from such an IFS-based representation. By using some existing similarity measures for IFSs, the proposed framework is applied to two benchmark datasets for text classification. The proposed framework yields satisfactory results when compared to decision tree, k-NN, naïve Bayes, and support vector machine classifiers.
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A new vector valued similarity measure for intuitionistic fuzzy sets based on OWA operators
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