PL-kNN: A Python']Python-based implementation of a parameterless k-Nearest Neighbors classifier

被引：8

作者：

Jodas, Danilo Samuel ^{[1
]}

Passos, Leandro Aparecido ^{[2
]}

Adeel, Ahsan ^{[2
]}

Papa, Joao Paulo ^{[1
]}

机构：

[1] Sao Paulo State Univ, Bauru, SP, Brazil

[2] Univ Wolverhampton, Sch Engn & Informat, Wolverhampton, England

来源：

SOFTWARE IMPACTS | 2023年 / 15卷

基金：

巴西圣保罗研究基金会; 英国工程与自然科学研究理事会;

关键词：

Machine learning; k-Nearest Neighbors; Classification; Clustering; !text type='Python']Python[!/text;

D O I：

10.1016/j.simpa.2022.100459

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页数：3

共 6 条

[1] k-Nearest Neighbour Classifiers - A Tutorial [J].