Abstract

Since the amount of information is rapidly growing, there is an overwhelming interest in efficient network computing. In this article, we take a detailed look at the problem of modelling and optimization of aforementioned systems for k-nearest neighbour classifier. First, we present a comprehensive discussion on considered classification methods with a special focus on improving classification accuracy or response time through the use of partitions of original data set for the nearest neighbour rule. Next, we propose a generic optimization model of a network computing system that can be used for distributed implementation of aforementioned recognition methods. The objective is to minimize the response time of the computing system applied for tasks related to k-nearest neighbours classifiers. We solve the problem using traditional branch and cut method and original algorithm GReTiMA based on a genetic approach as well. To illustrate our work, we provide results of numerical experiments showing the performance of the evolutionary approach compared against optimal results. Moreover, we show that the distributed approach enables significant improvement of the system response time