Class-Specific Feature Selection for One-Against-All Multiclass SVMs

This paper proposes a method to perform class-specific feature selection in multiclass support vector machines addressed with the one-against-all strategy. The main issue arises at the final step of the classification process, where binary classifier outputs must be compared one against another to elect the winning class. This comparison may be biased towards one specific class when the binary classifiers are built on distinct feature subsets. This paper proposes a normalization of the binary classifiers outputs that allows fair comparisons in such cases.