Improvement of neural network classifier using floating centroids

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092949" target="_blank" >RIV/61989100:27240/12:86092949 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10115-011-0410-8" target="_blank" >http://dx.doi.org/10.1007/s10115-011-0410-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-011-0410-8" target="_blank" >10.1007/s10115-011-0410-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improvement of neural network classifier using floating centroids

Popis výsledku v původním jazyce

This paper presents a novel technique-Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship between centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached tothese centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance

Název v anglickém jazyce

Improvement of neural network classifier using floating centroids

Popis výsledku anglicky

This paper presents a novel technique-Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship between centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached tothese centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F0990" target="_blank" >GA201/09/0990: Zpracování XML dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

433-454

Kód UT WoS článku

000304116100002

EID výsledku v databázi Scopus

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