Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification

Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification

This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of recent technology and engineering 2019-09, Vol.8 (3), p.60-68
Hauptverfasser: Sarwo, Heryadi, Yaya, Budiharto, Widodo, Abdurachman, Edi
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 68
container_issue 3
container_start_page 60
container_title International journal of recent technology and engineering
container_volume 8
creator Sarwo
Heryadi, Yaya
Budiharto, Widodo
Abdurachman, Edi
description This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.
doi_str_mv 10.35940/ijrte.C3876.098319
format Article
fullrecord <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_35940_ijrte_C3876_098319</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_35940_ijrte_C3876_098319</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1599-3326e3af29e30f659a9cd3eeabfc0fbce3624942d7a3c7ec42bc6938fdb071483</originalsourceid><addsrcrecordid>eNpNkNFKwzAYhYMoOOaewJu8QGuSv02bSylTB0O9UG9Lkv4ZGW0jSSfs7Z2diFff4cA5Fx8ht5zlUKqC3fl9nDBvoK5kzlQNXF2QhRBVlZ2q-vJfviarlPaMMQ6SFyAXxL7GYLTxvZ-O9CNMftxlRifs6HpMOJgeaXC0CeNX6A-TD6Pu6TMe4gkjTok2vU7JO48xURci3Qx6h3-t1T-TG3LldJ9w9csleX9YvzVP2fblcdPcbzPLS6UyACERtBMKgTlZKq1sB4jaOMucsQhSFKoQXaXBVmgLYaxUULvOsIoXNSwJnH9tDClFdO1n9IOOx5azdlbVzqraWVV7VgXfG_JgSg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Sarwo ; Heryadi, Yaya ; Budiharto, Widodo ; Abdurachman, Edi</creator><creatorcontrib>Sarwo ; Heryadi, Yaya ; Budiharto, Widodo ; Abdurachman, Edi ; Computer Science Study Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia ; Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia</creatorcontrib><description>This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.</description><identifier>ISSN: 2277-3878</identifier><identifier>EISSN: 2277-3878</identifier><identifier>DOI: 10.35940/ijrte.C3876.098319</identifier><language>eng</language><ispartof>International journal of recent technology and engineering, 2019-09, Vol.8 (3), p.60-68</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Sarwo</creatorcontrib><creatorcontrib>Heryadi, Yaya</creatorcontrib><creatorcontrib>Budiharto, Widodo</creatorcontrib><creatorcontrib>Abdurachman, Edi</creatorcontrib><creatorcontrib>Computer Science Study Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia</creatorcontrib><creatorcontrib>Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia</creatorcontrib><title>Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification</title><title>International journal of recent technology and engineering</title><description>This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.</description><issn>2277-3878</issn><issn>2277-3878</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpNkNFKwzAYhYMoOOaewJu8QGuSv02bSylTB0O9UG9Lkv4ZGW0jSSfs7Z2diFff4cA5Fx8ht5zlUKqC3fl9nDBvoK5kzlQNXF2QhRBVlZ2q-vJfviarlPaMMQ6SFyAXxL7GYLTxvZ-O9CNMftxlRifs6HpMOJgeaXC0CeNX6A-TD6Pu6TMe4gkjTok2vU7JO48xURci3Qx6h3-t1T-TG3LldJ9w9csleX9YvzVP2fblcdPcbzPLS6UyACERtBMKgTlZKq1sB4jaOMucsQhSFKoQXaXBVmgLYaxUULvOsIoXNSwJnH9tDClFdO1n9IOOx5azdlbVzqraWVV7VgXfG_JgSg</recordid><startdate>20190930</startdate><enddate>20190930</enddate><creator>Sarwo</creator><creator>Heryadi, Yaya</creator><creator>Budiharto, Widodo</creator><creator>Abdurachman, Edi</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20190930</creationdate><title>Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification</title><author>Sarwo ; Heryadi, Yaya ; Budiharto, Widodo ; Abdurachman, Edi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1599-3326e3af29e30f659a9cd3eeabfc0fbce3624942d7a3c7ec42bc6938fdb071483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Sarwo</creatorcontrib><creatorcontrib>Heryadi, Yaya</creatorcontrib><creatorcontrib>Budiharto, Widodo</creatorcontrib><creatorcontrib>Abdurachman, Edi</creatorcontrib><creatorcontrib>Computer Science Study Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia</creatorcontrib><creatorcontrib>Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of recent technology and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sarwo</au><au>Heryadi, Yaya</au><au>Budiharto, Widodo</au><au>Abdurachman, Edi</au><aucorp>Computer Science Study Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia</aucorp><aucorp>Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification</atitle><jtitle>International journal of recent technology and engineering</jtitle><date>2019-09-30</date><risdate>2019</risdate><volume>8</volume><issue>3</issue><spage>60</spage><epage>68</epage><pages>60-68</pages><issn>2277-3878</issn><eissn>2277-3878</eissn><abstract>This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.</abstract><doi>10.35940/ijrte.C3876.098319</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2277-3878
ispartof International journal of recent technology and engineering, 2019-09, Vol.8 (3), p.60-68
issn 2277-3878
2277-3878
language eng
recordid cdi_crossref_primary_10_35940_ijrte_C3876_098319
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
title Probability Voting-based Ensemble of Convolutional Neural nets Classifiers for Image Classification
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T17%3A19%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Probability%20Voting-based%20Ensemble%20of%20Convolutional%20Neural%20nets%20Classifiers%20for%20Image%20Classification&rft.jtitle=International%20journal%20of%20recent%20technology%20and%20engineering&rft.au=Sarwo&rft.aucorp=Computer%20Science%20Study%20Program,%20School%20of%20Computer%20Science,%20Bina%20Nusantara%20University,%20Jakarta,%20Indonesia&rft.date=2019-09-30&rft.volume=8&rft.issue=3&rft.spage=60&rft.epage=68&rft.pages=60-68&rft.issn=2277-3878&rft.eissn=2277-3878&rft_id=info:doi/10.35940/ijrte.C3876.098319&rft_dat=%3Ccrossref%3E10_35940_ijrte_C3876_098319%3C/crossref%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true