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A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples

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Bruzzone, L., & Persello, C. (2009). A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2142-2154. doi:10.1109/TGRS.2008.2011983.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C3DD-0
This paper presents a novel context-sensitive semisupervised support vector machine (CS4VM) classifier, which is aimed at addressing classification problems where the available training set is not fully reliable, i.e., some labeled samples may be associated to the wrong information class (mislabeled patterns). Unlike standard context-sensitive methods, the proposed CS4VM classifier exploits the contextual information of the pixels belonging to the neighborhood system of each training sample in the learning phase to improve the robustness to possible mislabeled training patterns. This is achieved according to both the design of a semisupervised procedure and the definition of a novel contextual term in the cost function associated with the learning of the classifier. In order to assess the effectiveness of the proposed CS4VM and to understand the impact of the addressed problem in real applications, we also present an extensive experimental analysis carried out on training sets that include different percentages of mislabeled patterns having different distributions on the classes. In the analysis, we also study the robustness to mislabeled training patterns of some widely used supervised and semisupervised classification algorithms (i.e., conventional support vector machine (SVM), progressive semisupervised SVM, maximum likelihood, and k-nearest neighbor). Results obtained on a very high resolution image and on a medium resolution image confirm both the robustness and the effectiveness of the proposed CS4VM with respect to standard classification algorithms and allow us to derive interesting conclusions on the effects of mislabeled patterns on different classifiers.