Improvement of FCM neural network classifier using K-Medoids clustering

Author

Xiaoqian Zhang ; Bo Yang ; Lin Wang ; Zhifeng Liang ; Abraham, Ajith

Author_Institution

Shandong Provincial Key Lab. of Network based Intell. Comput., Univ. of Jinan, Jinan, China

fYear

2014

fDate

July 30 2014-Aug. 1 2014

Firstpage

47

Lastpage

52

Abstract

Floating Centroids Method (FCM) is a new method to improve the performance of neural network classifier. But the K-Means clustering algorithm used in FCM is sensitive to outliers. So this weakness will influence the performance of classifier to a certain extent. In this paper, K-Medoids clustering algorithm which can diminish the sensitivity to the outliers is used to partition the mapping points into some disjoint subsets to improve FCM´s robustness and performance. Some data sets from UCI Machine Learning Repository are employed in our experiments. The results show a better performance for the FCM using our improved method.

Keywords

neural nets; pattern classification; pattern clustering; FCM neural network classifier; K-medoids clustering; UCI machine learning repository; floating centroids method; mapping points partitioning; Clustering algorithms; Color; Iris; Robustness; Sensitivity; Vehicles; Floating Centroids Method; K-means; K-medoids; classification; clustering; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on

Conference_Location

Porto

Print_ISBN

978-1-4799-5936-5

Type

conf

DOI

10.1109/NaBIC.2014.6921852

Filename

6921852