The distance function effect on k-nearest neighbor classification for medical datasets

Li-Yu Hu,Min-Wei Huang,Chih-Fong Tsai,Shih-Wen Ke

doi:10.1186/s40064-016-2941-7

Li-Yu Hu, Min-Wei Huang + Show 2 more

Open Access

https://doi.org/10.1186/s40064-016-2941-7

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Abstract

IntroductionK-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.Case descriptionSince the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.Discussion and evaluationThe experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.ConclusionsIn this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Highlights

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems
In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier
For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best

Summary

Introduction

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. The k-nearest neighbor (k-NN) algorithm is one of the most widely used classification algorithms since it is simple and easy to implement It is usually used as the baseline classifier in many domain problems (Jain et al 2000). The k-NN algorithm is a non-parametric method, which is usually used for classification and regression problems It is a type of lazy learning algorithms that off-line training is not needed. During the classification stage for a given testing example, the k-NN algorithm directly searches through all the training examples by calculating the distances

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