Classification based on Gaussian-kernel Support Vector Machine with Adaptive Fuzzy Inference System

• Autorzy: Abukhait J. , Mansour A. M. , Obeidat M.

Identyfikatory

DOI

10.15199/48.2018.05.03

Warianty tytułu

PL

Metoda klasyfikacji bazująca na SVM I adaptacyjnej logice rozmytej

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a new classification approach which combines the advantages of both Gaussian-kernel Support Vector Machine and Adaptive Fuzzy Inference System. Instead of generating a large number of candidate rules as in fuzzy classification, the proposed method adopts the decision trees to generate rules directly from training data. Decision trees provide architecture to generate fuzzy IF–THEN rules from the training data where the fuzzy parameters of the rules would be optimized using Genetic Algorithm. The Gaussian-kernel SVM will be used in the classification phase using the parameters obtained from Particle Swarm Optimization. Experimental results of the proposed approach has proved significantly better accuracy than other state-of-the-art classification methods by testing it on benchmark UCI datasets

PL

Zaproponowano nową metodę klasyfikacji łączącą zalety metod: Gaussian-Kernel Support Vector Machine i Adaptive Fuzzy Interference System. Wykorzystano drzewo decyzyjne do tworzenia zasad klasyfikacji bezpośrednio z danych treningowych. Parametry logiki rozmytej określano wykorxzystując algorytm genetyczny. A parametry SVM wykorzystując lagorytm mrówkowy.

Słowa kluczowe

EN

support vector machine; membership functions; fuzzy inference system; decision tree; genetic algorithm

PL

klasyfikacja danych; metoda SVM; logika rozmyta

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2018
• Tom: R. 94, nr 5
• Strony: 14--22
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Abukhait J.

• Department of Communication, Electronics and Computer Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan

autor

Mansour A. M.

• Department of Communication, Electronics and Computer Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan

autor

Obeidat M.

• Department of Communication, Electronics and Computer Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan

Bibliografia

• [1] Zadeh L. A., Fuzzy Sets, Information and Control, 8 (1965), No. 3, 338-353.
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• [4] Cao H., Wang Y., Jia L., Adaptive Neuro-Fuzzy Inference System-Based Pulverizing Capability Model for Running Time Assessment of Ball Mill Pulverizing System, Przegląd Elektrotechniczny , 89 (2013), No. 5, 122-127.
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• [8] Christianini N, Shawe-Taylor J., An introduction to support Vector Machines: and other kernel-based learning methods, Newyork 1999.
• [9] Vapnik V. N., The Nature of Statistical Learning Theory, USA: Springer-Verlag, 1995.
• [10] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods. New York, NY, USA: Cambridge University Press, 2000.
• [11] Kennedy J., Eberhart R., Particle swarm optimization, IEEE International Conference on Neural Networks, 4 (1995), 19421948.
• [12] Sheng Ding and Shunxin Li, PSO parameters optimization based support vector machines for hyperspectral classification, 2009 1st International Conference on Information Science and Engineering (ICISE), Nanjing, 2009, 4066-4069.
• [13] Eberhart R. C., Shi Y., Kennedy J., Swarm Intelligence (the Morgan Kaufmann Series in Evolutionary Computation). Morgan Kaufmann, 2001.
• [14] Cintra, M. E., Monard, M. C., Camargo, H. A., A Fuzzy decision tree algorithm based on C4.5, Mathware & Soft Computing Magazine. 20 (2013), No. 1, 56-62.
• [15] Ardjani F., Sadouni K., Benyettou M., Optimization of SVM MultiClass by particle swarm (PSO-SVM), 2010 2nd International Workshop on Database Technology and Applications (DBTA), Wuhan, 2010, 1-4.
• [16] De Souza B. F., De Carvalho A. C. P. L. F., Calvo R., Ishii R. P., Multiclass SVM model selection using particle swarm optimization, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06), Rio de Janeiro, Brazil, 2006, 3136.
• [17] JAJCZYK J., Optimisation using a parallelised genetic algorithm on a personal computer, Przegląd Elektrotechniczny , 91 (2015), No. 7, 36-38.
• [18] Hekim M., ANN-based classification of EEG signals using the average power based on rectangle approximation window, Przegląd Elektrotechniczny , 88 (2012), No.18, 210-215.
• [19] Blake C., Merz C., UCI repository of machine learning databases.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).