A new method for selection optimum k value in k-NN classification algorithm

Author

Maleki, Mehdi ; Eroglu, K. ; Aydemir, O. ; Manshoori, N. ; Kayikcioglu, T.

Author_Institution

Elektrik-Elektron. Muhendisligi Bolumu, Karadeniz Teknik Univ., Trabzon, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

Abstract

In this paper a new algorithm to calculate optimum value of k for k-nearest neighborhood (k-NN) is proposed. Selection of k value is very important in k-NN classification algorithm. Our algorithm applied to sub-sampling and K-fold cross validation methods, separately. We applied our algorithm in different distribution of data set with different variances and means. We compared our algorithm with other classical k selection algorithms. The results show that the proposed algorithm achieved better performance than the classical algorithms.

Keywords

learning (artificial intelligence); pattern classification; sampling methods; K-fold cross validation method; data set distribution; k-NN classification algorithm; k-nearest neighborhood; optimum k value selection; subsampling; Accuracy; Classification algorithms; Conferences; Face recognition; MATLAB; Speech recognition; K-fold cross validation; k-nearest neighborhood; leave-one-out; optimum k; pattern recognition; sub-samplin;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531324

Filename

6531324