PL-kNN: A Python-based implementation of a parameterless <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e43" altimg="si5.svg"> <mml:mi>k</mml:mi> </mml:math> -Nearest Neighbors classifier
Danilo Samuel Jodas, Leandro A. Passos, Ahsan Adeel, João Paulo Papa
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.
Topics & Concepts
Python (programming language)Scalable Vector GraphicsComputer scienceSource codeOpen sourcek-nearest neighbors algorithmAlgorithmProgramming languageData miningArtificial intelligenceSoftwareOperating systemMachine Learning and Data ClassificationAdvanced Statistical Methods and ModelsAnomaly Detection Techniques and Applications