An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources

TL;DR

This paper introduces an Arithmetic Method Regression (AMR) algorithm that optimizes k-Nearest Neighbors (k-NN) regression by leveraging an arithmetic method, demonstrating comparable or superior performance on real-world datasets.

Contribution

The paper proposes a novel AMR algorithm that enhances k-NN regression using an arithmetic method, providing an effective optimization approach.

Findings

AMR performs comparably to existing algorithms.

AMR often outperforms standard k-NN.

The proposed method is effective on real-world data.

Abstract

Linear regression analysis focuses on predicting a numeric regressand value based on certain regressor values. In this context, k-Nearest Neighbors (k-NN) is a common non-parametric regression algorithm, which achieves efficient performance when compared with other algorithms in literature. In this research effort an optimization of the k-NN algorithm is proposed by exploiting the potentiality of an introduced arithmetic method, which can provide solutions for linear equations involving an arbitrary number of real variables. Specifically, an Arithmetic Method Algorithm (AMA) is adopted to assess the efficiency of the introduced arithmetic method, while an Arithmetic Method Regression (AMR) algorithm is proposed as an optimization of k-NN adopting the potentiality of AMA. Such algorithm is compared with other regression algorithms, according to an introduced optimal inference decision rule, and evaluated on certain real world data sources, which are publicly available. Results are promising since the proposed AMR algorithm has comparable performance with the other algorithms, while in most cases it achieves better performance than the k-NN. The output results indicate that introduced AMR is an optimization of k-NN.