An ensemble-driven k-NN approach to ill-posed classification problems

Authors:
Mingmin Chi;Lorenzo Bruzzone
Affiliations:
DIT, University of Trento, Via Sommarive 14, I-38050 Trento, Italy;DIT, University of Trento, Via Sommarive 14, I-38050 Trento, Italy
Venue:
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Year:
2006

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Abstract

This paper addresses the supervised classification of remote-sensing images in problems characterized by relatively small-size training sets with respect to the input feature space and the number of classifier parameters (ill-posed classification problems). An ensemble-driven approach based on the k-nearest neighbor (k-NN) classification technique is proposed. This approach effectively exploits semilabeled samples (i.e., original unlabeled samples labeled by the classification process) to increase the accuracy of the classification process. Experimental results obtained on ill-posed classification problems confirm the effectiveness of the proposed approach, which significantly increases both the accuracy and the reliability of classification maps. ion maps.