Cell algorithms with data inflation for non-parametric classification

Authors:
Alessandro Palau;Farid Melgani;Sebastiano B. Serpico
Affiliations:
Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia, 11a, I-16145 Genova, Italy;Department of Information and Communication Technologies, University of Trento, Via Sommarive, 14, I-38050 Trento, Italy;Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia, 11a, I-16145 Genova, Italy
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

The k-nearest neighbor (k-NN) classifier represents one of the most popular non-parametric classification tools. Its main drawback is the computational cost required during the search for the nearest neighbors. In this paper, we propose using two cell algorithms with data inflation as tools capable to achieve interesting tradeoffs between classification error and computational cost. The performances of the proposed algorithms are assessed experimentally on the basis of a multisensor remotely sensed image and a pen-based handwritten digit data set.