A new approach for increasing K-nearest neighbors performance

Abstract

K-nearest neighbors is one of the most popular classification algorithms. It assumes that similar things or people are near to each other. One of the primordial steps in this algorithm is K value, which is given by the user. This value influences the algorithm result performance. In this paper, we suggest an enhanced approach that eliminates the use of K value with keeping the same performance and increasing it for a specific datasets type. We propose a combined approach named ‘Zone classifier’ that provides an excellent performance which is estimated, in average, by more than 85%; for Iris, Wine, Digits, The breast cancer and Olivetti faces dataset.