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Title: Automatic generation of fuzzy classification rules using granulation-based adaptive clustering
Authors: Al-Shammaa, M
Abbod, MF
Keywords: Fuzzy systems;Data classification;Data clustering;Granular computing
Issue Date: 2015
Publisher: IEEE
Citation: Proceedings of the 9th Annual IEEE International Systems Conference, SysCon 2015 - pp. 653 - 659, Vancouver, BC, (13-16 April 2015 )
Abstract: A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used.
ISBN: 9781479959273
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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