Ensemble methods, which combine generic binary classifier scores to generate a multi-classification output, are commonly used in state-of-the-art computer vision and pattern recognition systems that rely on multi-classification. In particular, we consider the one-vs-one decomposition of the multi-class problem, where binary classifier models are trained to discriminate every class pair. We describe a robust multi-classification pipeline, which at a high level involves projecting binary classifier scores into compact orthogonal subspaces, followed by a non-linear probabilistic multi-classification step, using Kernel Density Estimation (KDE). We compare our approach against state-of-the-art ensemble methods (DCS, DRCW) on 16 multi-class datasets. We also compare against the most commonly used ensemble methods (VOTE, NEST) on 6 real-world computer vision datasets. Finally, we measure the statistical significance of our approach using non-parametric tests. Experimental results show that our approach gives a statistically significant improvement in multi-classification performance over state-of-the-art.

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robust multiclassification pipeline stateoftheart ensemble methods statistical nonlinear probabilistic multiclassification step stateoftheart computer vision and pattern recognition systems nonparametric italiconevsoneitalic decomposition of the multiclass problem generic binary classifier scores orthogonal subspaces kernel density estimation

Venkataraman Santhanam, David Harwood, Vlad I. Morariu, Larry S. Davis,.A non-parametric approach to extending generic binary classifiers for multi-classification. 58 (0),149-158.

Venkataraman Santhanam, et al."A non-parametric approach to extending generic binary classifiers for multi-classification" 58

Venkataraman Santhanam, David Harwood, Vlad I. Morariu, Larry S. Davis,"A non-parametric approach to extending generic binary classifiers for multi-classification"