IPADE: iterative prototype adjustment for nearest neighbor classification

Authors:
Isaac Triguero;Salvador García;Francisco Herrera
Affiliations:
CITIC-UGR, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science, University of Jaén, Jaén, Spain;CITIC-UGR, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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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, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric 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 NN classifier.