High-dimensional data classification model based on random projection and Bagging-support vector machine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246215" target="_blank" >RIV/61989100:27240/20:10246215 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.6095" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.6095</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cpe.6095" target="_blank" >10.1002/cpe.6095</a>

Jazyk výsledku

angličtina

Název v původním jazyce

High-dimensional data classification model based on random projection and Bagging-support vector machine

Popis výsledku v původním jazyce

Aiming at the long training time when classifying high-dimensional data, a parallel classification model is proposed based on random projection and Bagging-support vector machine (SVM) to process high-dimensional data. The model first uses random projection to project the input data into the low-dimensional space. Then, we used the Bagging method to construct multiple training data subsets and used SVM to train the training subset in parallel and generate several subclassifiers. Finally, various classifiers vote to determine the category of the test sample. The model has been verified using two standard datasets. The experimental results show that the model can significantly improve the training speed and classification performance of high-dimensional data with little accuracy loss. (C) 2020 John Wiley &amp; Sons Ltd

Název v anglickém jazyce

High-dimensional data classification model based on random projection and Bagging-support vector machine

Popis výsledku anglicky

Aiming at the long training time when classifying high-dimensional data, a parallel classification model is proposed based on random projection and Bagging-support vector machine (SVM) to process high-dimensional data. The model first uses random projection to project the input data into the low-dimensional space. Then, we used the Bagging method to construct multiple training data subsets and used SVM to train the training subset in parallel and generate several subclassifiers. Finally, various classifiers vote to determine the category of the test sample. The model has been verified using two standard datasets. The experimental results show that the model can significantly improve the training speed and classification performance of high-dimensional data with little accuracy loss. (C) 2020 John Wiley &amp; Sons Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Concurrency Computation Practice and Experience

ISSN

1532-0626

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000591646500001

EID výsledku v databázi Scopus

2-s2.0-85096690830