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The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Classifiers
Bekir Karlik
Pages - 1 - 8     |    Revised - 31-12-2015     |    Published - 01-02-2016
Volume - 7   Issue - 1    |    Publication Date - January / February 2016  Table of Contents
Fuzzy C-Means Clustering, ANN, SVM, Learning, Classifier.
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
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1 I.O. Bucak, B. Karlik, “Detection of Drinking Water Quality Using CMAC Based Artificial Neural Networks.” Ekoloji, vol. 20(78), pp. 75-81, 2011.
2 B. Karlik, “Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers.” BURCH Journal of Science and Technology, vol. 1(1), pp. 49-62, 2011.
3 B. Karlik, “Machine Learning Algorithms for Characterization of EMG Signals.” International Journal of Information and Electronics Engineering, vol. 4, no. 3, pp. 189-194, May 2014.
4 B. Karlik, “Soft Computing Methods in Bioinformatics: A Comprehensive Review.” Mathematical & Computational Applications, vol.18, no.3, pp. 176-197, 2013.
5 B. Karlik, H. Torpi, M. Alci, “A Fuzzy-Neural Approach for the Characterization of the Active Microwave Devices.” Proceeding of CriMiCo’02, Sevastopol, Ukraine, September 9-14, 2002, pp. 9-14.
6 R.H. Abiyev, O. Kaynak, “Identification and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks.” IEEE International Symposium on Intelligent Control (ISIC 2008), San Antonio, TX, USA, September 3-5, 2008, pp.1295-1301.
7 M. Korürek, B. Dogan, “ECG Beat Classification Using Particle Swarm Optimization and Radial Basis Function Neural Network.” Expert Systems with Applications, vol. 37, no. 12, pp. 7563-7569, 2010.
8 B. Dogan, M. Korürek, “A new ECG Beat Clustering Method Based on Kernelized Fuzzy C- Means and Hybrid Ant Colony Optimization for Continuous Domains.” Applied Soft Computing, vol. 12, no. 11, pp. 3442-3451, 2012.
9 B. Karlik, O. Tokhi, M. Alci, “A Fuzzy Clustering Neural Network Architecture for Multi-Function Upper-Limb Prosthesis.” IEEE Transactions on Biomedical Engineering, vol. 50, no. 11, pp. 1255-1261, 2003.
10 Y. Özbay, R. Pektatli, B. Karlik, “A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias.”, Computers in Biology and Medicine, vol. 36, pp.376–388, 2006.
11 B. Karlik, K. Yüksek, “Fuzzy Clustering Neural Networks for Real Time Odor Recognition System.” Journal of Automated Methods and Management in Chemistry, Article ID 38405, doi:10.1155/2007/38405, Dec. 2007.
12 B. Karlik, M. Korürek, Y. Koçyigit, “Differentiating Types of Muscle Movements using Wavelet Based Fuzzy Clustering Neural Network.” Expert Systems, vol. 26(1), pp. 49-59, 2009.
13 R. Ceylan, Y. Özbay, B. Karlik, “A Novel Approach for Classification of ECG Arrhythmias: Type-2 Fuzzy Clustering Neural Network.” Expert Systems with Applications, vol. 36, issue. 3, part. 2, pp. 6721-6726, 2009.
14 R.H. Abiyev, O. Kaynak, “Type-2 Fuzzy Neural Structure for Identification and Control of Time-varying Plants.” IEEE Trans.on Industrial Electronics, vol.57(12), pp. 4147-4159, 2010.
15 R. Ceylan, Y. Özbay, B. Karlik, “Telecardiology and Teletreatment System Design for Heart Failures Using Type-2 Fuzzy Clustering Neural Networks.” International Journal of Artificial Intelligence and Expert Systems vol. 1(4), pp. 100-110, 2011.
16 R.H. Abiyev, O. Kaynak, T. Alshanableh, F. Mamedov, “A type-2 Neuro-Fuzzy System Based on Clustering and Gradient Techniques Applied to System Identification and Channel Equalization.” Applied Soft Computing, vol. 11(1), pp. 1396-1406, 2011.
17 Y. Özbay, R. Ceylan, B. Karlik, “Integration of Type-2 Fuzzy Clustering and Wavelet Transform in a Neural Network Based ECG Classifier.” Expert Systems with Applications, vol. 38, pp. 1004-1010, 2011.
18 R. Ceylan Rahime, Y. Özbay, B. Karlik, “Comparison of Type-2 Fuzzy Clustering Based Cascade Classifier Models for ECG Arrhythmias.” Biomedical Engineering: Applications, Basis and Communications (BME), vol. 26, no. 6, 2014-1450075, 2014.
19 O. Ornek, A. Subasi, “Clustering Marketing Datasets with Data Mining Techniques.” The 2nd Inter. Symposium on Sustainable Development, Sarajevo, Bosnia and Herzegovina, June 8-9, 2010, vol. 3, pp: 408-412.
20 J.C. Bezdek, R. Ehrlich, W. Full, “FCM: The Fuzzy C-Means Clustering Algorithm.” Computers & Geosciences, vol. 10, pp. 191–203, 1984.
21 J. Fan, W. Zhen, and W. Xie, “Supervised Fuzzy C-Means Clustering Algorithm.” Elsevier Science Pattern Recognition Letters, vol. 24, pp. 1607-1612, 2003.
22 F.C.H. Rhee, C. Hwang, “A Type-2 Fuzzy C-Means Clustering Algorithm.” IEEE Transaction on Neural Networks, vol. 9, no. 1, pp. 83-105, 2001.
23 B. Karlik, “The Effects of Fuzzy Clustering on the Back-Propagation Algorithm.” International Conference on Computational and Applied Mathematics, Ukraine, Abstract Book, pp. 9-10 September, 2002, Kiev, Ukraine.
24 B. Karlik, “Differentiating Type of Muscle Movement via AR Modeling and Neural Networks Classification." Turk J Elec Eng & Comp Sci, vol. 7, pp. 45-52, 1999.
25 I.O. Bucak, “Performance Evaluation of Neural Classifiers through Confusion Matrices to Diagnose Skin Conditions.” International Journal of Artificial Intelligence and Expert Systems (IJAE), vol.5, Issue: 2, pp. 15–27, 2014.
26 E. Esme and B. Karlik, “FCM Based SVM Classifier for Perfume Recognition.” Applied Soft Computing (re-submitted).
27 B. Karlik, Y. Bastaki, “Real Time Monitoring Odor Sensing System Using OMX-GR Sensor and Neural Network.” WSEAS Transactions on Electronics, issue 2, vol.1, pp.337-342, 2004.
Professor Bekir Karlik
Selcuk University, Department of Computer Engineering - Turkey