A hierarchical multi-class classification system for face and text datasets

dc.contributor.authorSaini, Ashish
dc.contributor.authorGill, Nasib Singh
dc.contributor.authorGulia, Preeti
dc.contributor.authorSingh, Khushwant
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2025-06-23T14:47:37Z
dc.date.available2025-06-23T14:47:37Z
dc.date.issued2025-06-20
dc.description.abstractIn an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This study provides an understanding of the organization of data, and feature selection (i.e., edge) using the k-means segmentation technique is explained. Furthermore, for the optimization of features, the linear regression technique is used. The optimized features can be directly used with classifiers, but to reduce the noise, outliers are identified and removed from the training data. The classifiers are involved in training and recognizing the face or text class label. After the prediction of class labels, the distance matrix-based technique is used to identify the style or pose name. Finally, the experiments are conducted with the help of the ORL dataset (40 classes and 10 poses in each class) and character dataset (36 characters and 10 font styles in each character). The experimental results indicated that the proposed methodology accurately classifies hierarchically organized data and demonstrates superiority over KNN and Bayesian-based classification when compared to support vector machine (SVM). The system provides classification outcomes with up to 100% accuracy for outlier-removed data, and up to 98% for basic features. Unlike traditional flat classification approaches, our system leverages hierarchical structures to enhance classification accuracy, scalability, and interpretability.
dc.identifier.citationSaini, A., Gill, N. S., Gulia, P., Singh, K., & Moreira, F. (2025). A hierarchical multi-class classification system for face and text datasets. Frontiers in Computer Science, 7, 1550453, 1-12. https://doi.org/10.3389/fcomp.2025.1550453. Repositório Institucional UPT. https://hdl.handle.net/11328/6401
dc.identifier.issn2624-9898
dc.identifier.urihttps://hdl.handle.net/11328/6401
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.relationREMIT - Research on Economics, Management and Information Technologies (UIDB/05105/2020)
dc.relation.hasversionhttps://doi.org/10.3389/fcomp.2025.1550453
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectData mining
dc.subjectsupport vector machine
dc.subjectBayes classifier
dc.subjectk-nearest neighbor
dc.subjectmachine learning
dc.subject.fosCiências Naturais - Ciências da Computação e da Informação
dc.titleA hierarchical multi-class classification system for face and text datasets
dc.typejournal article
dcterms.referenceshttps://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1550453/full
dspace.entity.typePublication
oaire.awardTitleREMIT - Research on Economics, Management and Information Technologies (UIDB/05105/2020)
oaire.awardURIhttps://hdl.handle.net/11328/5403
oaire.citation.endPage12
oaire.citation.startPage1
oaire.citation.titleFrontiers in Computer Science
oaire.citation.volume7
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e
relation.isProjectOfPublication916295ec-caa8-4105-9f18-6516c646e7a8
relation.isProjectOfPublication.latestForDiscovery916295ec-caa8-4105-9f18-6516c646e7a8

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