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| 題名: | The distance function effect on k-nearest neighbor classification for medical datasets |
| 作者: | 柯士文;Hu, Li-Yu;Huang, Min-Wei;Ke, Shih-Wen;Tsai, Chih-Fong |
| 貢獻者: | 管理學院資訊管理學系 |
| 關鍵詞: | Case Study;Classification;Computer Science;Humanities and Social Sciences;multidisciplinary;Science;Science (multidisciplinary) |
| 日期: | 2016-12-01 |
| 上傳時間: | 2026-04-23 13:55:40 (UTC+8) |
| 出版者: | Springer Science and Business Media Deutschland GmbH;Cham: Springer Science and Business Media LLC |
| 摘要: | 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： Springer Nature Link
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 |
| 顯示於類別: | [資訊管理學系] 期刊論文
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