Back كي أقرب جار Arabic Knn Catalan کەی نزیکترین ھاوسێکان CKB Algoritmus k-nejbližších sousedů Czech K-nærmeste naboer Danish Nächste-Nachbarn-Klassifikation German K vecinos más próximos Spanish K auzokide hurbilenak Basque الگوریتم کی-نزدیک‌ترین همسایه Persian Méthode des k plus proches voisins French

K-nearest neighbors algorithm

This article is about supervised classification/regression, but NOT a clustering algorithm. For algorithms for finding nearest neighbors, see Nearest neighbor search.
Not to be confused with k-means clustering.

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951,[1] and later expanded by Thomas Cover.[2] It is used for classification and regression. In both cases, the input consists of the k closest training examples in a data set. The output depends on whether k-NN is used for classification or regression:

  • In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
  • In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors. If k = 1, then the output is simply assigned to the value of that single nearest neighbor.

k-NN is a type of classification where the function is only approximated locally and all computation is deferred until function evaluation. Since this algorithm relies on distance for classification, if the features represent different physical units or come in vastly different scales then normalizing the training data can improve its accuracy dramatically.[3]

Both for classification and regression, a useful technique can be to assign weights to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.[4]

The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.

A peculiarity of the k-NN algorithm is that it is sensitive to the local structure of the data.

  1. ^ Fix, Evelyn; Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties (PDF) (Report). USAF School of Aviation Medicine, Randolph Field, Texas. Archived (PDF) from the original on September 26, 2020.
  2. ^ Cite error: The named reference :1 was invoked but never defined (see the help page).
  3. ^ Hastie, Trevor. (2001). The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations. Tibshirani, Robert., Friedman, J. H. (Jerome H.). New York: Springer. ISBN 0-387-95284-5. OCLC 46809224.
  4. ^ This scheme is a generalization of linear interpolation.

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