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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
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KEYWORDS
Fuzzy C-Means Clustering, ANN, SVM, Learning, Classifier.
ABSTRACT
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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Professor Bekir Karlik
Selcuk University, Department of Computer Engineering - Turkey
bkarlik@selcuk.edu.tr


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