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Conference Papers Year : 2010

Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM

Abstract

We try to overcome the imbalanced data set problem in image annotation by choosing a convenient loss function for learning the classifier. Instead of training a standard SVM, we use a Ranking SVM in which the chosen loss function is helpful in the case of imbalanced data. We compare the Ranking SVM to a classical SVM with different visual features. We observe that Ranking SVM always improves the prediction quality, and can perform up to 23% better than the classical SVM.

Dates and versions

hal-00581661 , version 1 (31-03-2011)

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Cite

Ali Fakeri-Tabrizi, Sabrina Tollari, Nicolas Usunier, Patrick Gallinari. Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM. CLEF 2009 - 10th Workshop of the Cross-Language Evaluation Forum, Sep 2009, Corfu, Greece. pp.291-294, ⟨10.1007/978-3-642-15751-6_37⟩. ⟨hal-00581661⟩
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