Benchmarking parameterized fuzzy c-Means classifier

This paper reports on the performance of the fuzzy c-means based classifier (FCMC). Test set performances optimized by way of several CV procedures and three sets of hyperparameters are throughly compared. UCI benchmark datasets are used to evaluate the performance. FCM classifier in combination wit...

Full description

Saved in:
Bibliographic Details
Published in2009 IEEE International Conference on Fuzzy Systems pp. 1137 - 1144
Main Authors Ichihashi, H., Nagaura, K., Notsu, A., Honda, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Benchmark testing
Clustering algorithms
Convergence
Error analysis
Iterative algorithms
Partitioning algorithms
Performance evaluation
Shape
Support vector machine classification
Support vector machines
Online AccessGet full text

Cover

More Information
Summary:This paper reports on the performance of the fuzzy c-means based classifier (FCMC). Test set performances optimized by way of several CV procedures and three sets of hyperparameters are throughly compared. UCI benchmark datasets are used to evaluate the performance. FCM classifier in combination with standard 10-CV procedure or resubstitution (i.e., 1-CV) procedure for parameter selection achieves good test set performance compared to k-nearest neighbor classifier (k-NN). Randomized test sets performance of the classifier is comparable to that of the support vector machine (SVM) reported in the literature.
ISBN:9781424435968
142443596X
ISSN:1098-7584
DOI:10.1109/FUZZY.2009.5277059