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

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/107082

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107082

Title:	The distance function effect on k-nearest neighbor classification for medical datasets
Authors:	蔡志豐;Hu, Li-Yu;Huang, Min-Wei;Ke, Shih-Wen;Tsai, Chih-Fong
Contributors:	管理學院資訊管理學系
Keywords:	Case Study;Classification;Computer Science;Humanities and Social Sciences;multidisciplinary;Science;Science (multidisciplinary)
Date:	2016-12-01
Issue Date:	2026-04-23 13:55:45 (UTC+8)
Publisher:	Springer Science and Business Media Deutschland GmbH;Cham: Springer Science and Business Media LLC
Abstract:	摘要： 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. Case description Since 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 evaluation The 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. Conclusions 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. 其他題名： SpringerPlus 其他題名： Springerplus 出版者： Cham: Springer Science and Business Media LLC 出版日期： 2016-08-09 出處： SpringerPlus, 2016-08, Vol.5 (1), p.1304-1304, Article 1304 資源來源： Agricultural & Environmental Science Collection 版權： The Author(s) 2016 版權： SpringerPlus is a copyright of Springer, 2016. 識別號： ISSN: 2193-1801 識別號： EISSN: 2193-1801 識別號： DOI: 10.1186/s40064-016-2941-7 識別號： PMID: 27547678
Appears in Collections:	[Department of Information Management] journal & Dissertation

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