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A multi-class extension for multi-labeler support vector machines

In recent years, there has been an increasing interest in the design of pattern recognition systems able to deal with labels coming from multiple sources. To avoid bias during the learning process, in some applications it is strongly recommended to learn from a set of panelists or experts instead of only one. In particular, two aspects are of interest, namely: discriminating between confident and unconfident labelers, and determining the suitable ground truth. This work presents an extension of a previous work, which consists of a generalization of the two class case via a modified one-against-all approach. This approach uses modified classifiers able to learn from multi-labeler settings. This is done within a soft-margin support vector machine framework. Proposed method provides ranking values for panelist as well as an estimate of the ground truth.