title: The distance function effect on k-nearest neighbor classification for medical datasets.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
authors.forename: Li-Yu
authors.initials: LY
authors.lastname: Hu
authors.affiliation: Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi, Taiwan.
authors.forename: Min-Wei
authors.initials: MW
authors.lastname: Huang
authors.affiliation: Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
authors.forename: Shih-Wen
authors.initials: SW
authors.lastname: Ke
authors.affiliation: Department of Information Management, National Central University, Taoyuan, Taiwan.
authors.forename: Chih-Fong
authors.initials: CF
authors.lastname: Tsai
keywords: Distance function; Euclidean distance; Medical datasets; Pattern classification; k-Nearest neighbor