文章基本信息

标题：The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Classifiers
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作者：Professor Bekir Karlik
期刊名称：International Journal of Artificial Intelligence and Expert Systems (IJAE)
电子版ISSN：2180-124X
出版年度：2016
卷号：7
期号：1
页码：1-8
出版社：Computer Science Journals
摘要：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.
关键词：Fuzzy C-Means Clustering; ANN; SVM; Learning; Classifier.