Improvement of FCM neural network classifier using K-Medoids clustering

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092756" target="_blank" >RIV/61989100:27240/14:86092756 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921852" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2014.6921852</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921852" target="_blank" >10.1109/NaBIC.2014.6921852</a>

Result language
angličtina
Original language name
Improvement of FCM neural network classifier using K-Medoids clustering
Original language description
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 certainextent. 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.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
NaBIC 2014 ; CASoN 2014 : July 30-31, Porto, Portugal
ISBN
978-1-4799-5937-2
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
47-52
Publisher name
IEEE
Place of publication
New York
Event location
Porto
Event date
Jul 30, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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