Multiclass classification based on the analytical center of version space

Analytical center machine, based on the analytical center of version space, outperforms support vector machine, especially when the version space is elongated or asymmetric. While analytical center machine for binary classification is well understood, little is known about corresponding multiclass classification. Moreover, considering that the current multiclass classification method: "one versus all" needs repeatedly constructing classifiers to separate a single class from all the others, which leads to daunting computation and low efficiency of classification, and that though multiclass support vector machine corresponds to a simple quadratic optimization, it is not very effective when the version space is asymmetric or elongated. Thus, the multiclass classification approach based on the analytical center of version space is proposed to address the above problems. Experiments on wine recognition and glass identification dataset demonstrate validity of the approach proposed.