IPADE: Iterative prototype adjustment for nearest neighbor classification

Abstract

Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction that allows the fitting of a data set for nearest neighbor classification. This concise paper presents a methodology to learn iteratively the positioning of prototypes using real parameters’ optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution (IPADE). The results obtained are contrasted with non-parametrical statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the nearest neighbor classifier.