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http://hdl.handle.net/1893/36789| Appears in Collections: | Computing Science and Mathematics Journal Articles |
| Peer Review Status: | Refereed |
| Title: | PL-kNN: A Python-based implementation of a parameterlessk-Nearest Neighbors classifier |
| Author(s): | Jodas, Danilo Samuel Passos, Leandro Aparecido Adeel, Ahsan Papa, João Paulo |
| Contact Email: | ahsan.adeel1@stir.ac.uk |
| Keywords: | machine learning k-nearest Neighbours Classification Clustering Python |
| Issue Date: | Mar-2023 |
| Date Deposited: | 7-Mar-2025 |
| Citation: | Jodas 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.2022.100459 |
| Abstract: | This 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. |
| DOI Link: | 10.1016/j.simpa.2022.100459 |
| Rights: | This 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. |
| Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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| 1-s2.0-S2665963822001439-main.pdf | Fulltext - Published Version | 351.91 kB | Adobe PDF | View/Open |
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