1. ZHAW digitalcollection
  2. School of Management and Law
  3. Publikationen
  4. Publikationen School of Management and Law
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25558
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNiroomand, Naghmeh-
dc.contributor.authorBach, Christian-
dc.contributor.authorElser, Miriam-
dc.date.accessioned2022-09-01T12:59:24Z-
dc.date.available2022-09-01T12:59:24Z-
dc.date.issued2021-
dc.identifier.issn2169-3536de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25558-
dc.description.abstractThis paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes. The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset. Experiment results demonstrate that the classification of SSFCM algorithm has better correlation with expert classification than traditional unsupervised algorithm. These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances. Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Accessde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAutomobilede_CH
dc.subjectClassification algorithmde_CH
dc.subjectClustering algorithmde_CH
dc.subjectFeature extractionde_CH
dc.subjectFuzzy C-means clusteringde_CH
dc.subjectSemi-supervised learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc629: Luftfahrt- und Fahrzeugtechnikde_CH
dc.titleRobust vehicle classification based on deep features learningde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitZentrum für Arbeitsmärkte, Digitalisierung und Regionalökonomie (CLDR)de_CH
dc.identifier.doi10.1109/ACCESS.2021.3094366de_CH
dc.identifier.doi10.21256/zhaw-25558-
zhaw.funding.euNode_CH
zhaw.originated.zhawNode_CH
zhaw.pages.end95685de_CH
zhaw.pages.start95675de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume9de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedW: Spitzenpublikationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Management and Law

Files in This Item:
File Description SizeFormat 
2021_Niroomand-etal_Robust-vehicle-classification-deep-features-learning.pdf6.9 MBAdobe PDFThumbnail
View/Open
Show simple item record
Niroomand, N., Bach, C., & Elser, M. (2021). Robust vehicle classification based on deep features learning. IEEE Access, 9, 95675–95685. https://doi.org/10.1109/ACCESS.2021.3094366
Niroomand, N., Bach, C. and Elser, M. (2021) ‘Robust vehicle classification based on deep features learning’, IEEE Access, 9, pp. 95675–95685. Available at: https://doi.org/10.1109/ACCESS.2021.3094366.
N. Niroomand, C. Bach, and M. Elser, “Robust vehicle classification based on deep features learning,” IEEE Access, vol. 9, pp. 95675–95685, 2021, doi: 10.1109/ACCESS.2021.3094366.
NIROOMAND, Naghmeh, Christian BACH und Miriam ELSER, 2021. Robust vehicle classification based on deep features learning. IEEE Access. 2021. Bd. 9, S. 95675–95685. DOI 10.1109/ACCESS.2021.3094366
Niroomand, Naghmeh, Christian Bach, and Miriam Elser. 2021. “Robust Vehicle Classification Based on Deep Features Learning.” IEEE Access 9: 95675–85. https://doi.org/10.1109/ACCESS.2021.3094366.
Niroomand, Naghmeh, et al. “Robust Vehicle Classification Based on Deep Features Learning.” IEEE Access, vol. 9, 2021, pp. 95675–85, https://doi.org/10.1109/ACCESS.2021.3094366.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.