A cost-effective semisupervised classifier approach with kernels

This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to pubspermissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. In this paper we propose a cost-effective iterative semi-supervised classifier based on a kernel concept. The proposed technique incorporates unlabeled data into the design of a binary classifier by introducing and optimizing a cost function in a feature space which maximizes the Rayleigh coefficient while minimizing the total cost associated with misclassified labeled samples. The cost assigned to misclassified labeled samples accounts for the number of misclassified labeled samples as well as the amount by which they are on the wrong side of the boundary, and this counter-balances any potential adverse effect of unlabeled data on the classifier performance. Several experiments performed with remotely-sensed data demonstrate that using the proposed semisupervised classifier shows considerable improvements over the supervised-only counterpart. I