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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36789
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dc.contributor.authorJodas, Danilo Samuelen_UK
dc.contributor.authorPassos, Leandro Aparecidoen_UK
dc.contributor.authorAdeel, Ahsanen_UK
dc.contributor.authorPapa, João Pauloen_UK
dc.date.accessioned2025-03-11T01:19:17Z-
dc.date.available2025-03-11T01:19:17Z-
dc.date.issued2023-03en_UK
dc.identifier.other100459en_UK
dc.identifier.urihttp://hdl.handle.net/1893/36789-
dc.description.abstractThis paper presents an open-source implementation of PL-kNN, a parameterless version of the k-Nearest Neighbors algorithm. The proposed model, developed in Python 3.6, was designed to avoid the choice of the k parameter required by the standard k-Nearest Neighbors technique. Essentially, the model computes the number of nearest neighbors of a target sample using the data distribution of the training set. The source code provides functions resembling the Scikit-learn methods for fitting the model and predicting the classes of the new samples. The source code is available in the GitHub repository with instructions for installation and examples for usage.en_UK
dc.language.isoenen_UK
dc.publisherElsevier BVen_UK
dc.relationJodas DS, Passos LA, Adeel A & Papa JP (2023) PL-kNN: A Python-based implementation of a parameterlessk-Nearest Neighbors classifier. <i>Software Impacts</i>, 15, Art. No.: 100459. https://doi.org/10.1016/j.simpa.2022.100459en_UK
dc.rightsThis is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectmachine learningen_UK
dc.subjectk-nearest Neighboursen_UK
dc.subjectClassificationen_UK
dc.subjectClusteringen_UK
dc.subjectPythonen_UK
dc.titlePL-kNN: A Python-based implementation of a parameterlessk-Nearest Neighbors classifieren_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1016/j.simpa.2022.100459en_UK
dc.citation.jtitleSoftware impactsen_UK
dc.citation.issn2665-9638en_UK
dc.citation.volume15en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailahsan.adeel1@stir.ac.uken_UK
dc.contributor.affiliationSao Paulo State University (Universidade Estadual Paulista)en_UK
dc.contributor.affiliationUniversity of Wolverhamptonen_UK
dc.contributor.affiliationUniversity of Wolverhamptonen_UK
dc.contributor.affiliationSao Paulo State University (Universidade Estadual Paulista)en_UK
dc.identifier.isiWOS:000912100400001en_UK
dc.identifier.scopusid2-s2.0-85145703582en_UK
dc.identifier.wtid2090011en_UK
dc.contributor.orcid0000-0002-0370-1211en_UK
dc.date.accepted2022-12-14en_UK
dcterms.dateAccepted2022-12-14en_UK
dc.date.filedepositdate2025-03-07en_UK
dc.relation.funderprojectCOG-MHEAR: Towards cognitively-inspired 5G-IoT enabled, multi-modal Hearing Aidsen_UK
dc.relation.funderref1753817en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorJodas, Danilo Samuel|0000-0002-0370-1211en_UK
local.rioxx.authorPassos, Leandro Aparecido|en_UK
local.rioxx.authorAdeel, Ahsan|en_UK
local.rioxx.authorPapa, João Paulo|en_UK
local.rioxx.project1753817|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2025-03-07en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2025-03-07|en_UK
local.rioxx.filename1-s2.0-S2665963822001439-main.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2665-9638en_UK
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