Classification of objects is an important area in a variety of fields and applications. Many different methods are available to make a decision in those cases. The <em>k</em>-nearest neighbor rule (<em>k-NN</em>) is a well-known nonparametric decision procedure. Classification rules based on the<em> k-NN</em><em> </em>have already been proposed and applied in diverse substantive areas. The editing <em>k-NN</em> proposed by Wilson would be an important one. In this rule, editing the reference set is first performed, every sample in the reference set is classified by using the<em> k-NN</em> rule and the set is formed by eliminating it from the reference set. All the samples mistakenly classified are then deleted from the reference set. Afterward, any input sample is classified using the <em>k-NN</em> rule and the edited reference set. Obviously, the editing <em>k</em>-nearest neighbors classifier (<em>EK -NN</em>) consists of the<em> k</em>-nearest neighbor classifier and an editing reference set. However, the editing reference set gained by this method is only a subset of the reference set. This may result in the loss of some important information and decline of classification accuracy. In this paper, we focus on modifying the editing reference set of <em>EK -NN</em>, the new editing set in our method consists of subsets of the reference set and testing set, such subsets are received by classifying every sample in the reference set and testing set by using the <em>k-NN</em> rule and removing misclassified samples from reference set and testing set, respectively. Advantages of our method are to reduce the loss of information and improve the recognition rate. Comparisons and analysis of the experimental results demonstrate the capability of the proposed algorithm