Soft granular computing based classification using hybrid fuzzy-KNN-SVM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099105" target="_blank" >RIV/61989100:27240/16:86099105 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/IDT-150243" target="_blank" >http://dx.doi.org/10.3233/IDT-150243</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/IDT-150243" target="_blank" >10.3233/IDT-150243</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Soft granular computing based classification using hybrid fuzzy-KNN-SVM

Popis výsledku v původním jazyce

This paper aims at providing the concept of information granulation in Granular computing based pattern classification that is used to deal with incomplete, unreliable, uncertain knowledge from the view of a dataset. Data Discretization provides us the granules which further can be used to classify the instances. We use Equal width and Equal frequency Discretization as unsupervised ones; Fayyad-Irani's Minimum description length and Kononenko's supervised discretization approaches along with Fuzzy logic, neural network, Support vector machine and their hybrids to develop an efficient granular information processing paradigm. The experimental results show the effectiveness of our approach. We use benchmark datasets in UCI Machine Learning Repository in order to verify the performance of granular computing based approach in comparison with other existing approaches. Finally, we perform statistical significance test for confirming validity of the results obtained. (C) 2016 IOS Press and the authors. All rights reserved.

Název v anglickém jazyce

Soft granular computing based classification using hybrid fuzzy-KNN-SVM

Popis výsledku anglicky

This paper aims at providing the concept of information granulation in Granular computing based pattern classification that is used to deal with incomplete, unreliable, uncertain knowledge from the view of a dataset. Data Discretization provides us the granules which further can be used to classify the instances. We use Equal width and Equal frequency Discretization as unsupervised ones; Fayyad-Irani's Minimum description length and Kononenko's supervised discretization approaches along with Fuzzy logic, neural network, Support vector machine and their hybrids to develop an efficient granular information processing paradigm. The experimental results show the effectiveness of our approach. We use benchmark datasets in UCI Machine Learning Repository in order to verify the performance of granular computing based approach in comparison with other existing approaches. Finally, we perform statistical significance test for confirming validity of the results obtained. (C) 2016 IOS Press and the authors. All rights reserved.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Intelligent Decision Technologies

ISSN

1872-4981

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

115-128

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84960844134