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A non-parametric approach to extending generic binary classifiers for multi-classification
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DOI：10.1016/j.patcog.2016.04.008.png)
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Abstract
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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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Multi-classification
Ensemble method
One-vs-one
Orthogonal subspace
Non-parametric density estimation
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