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Robust Learning of Multi-Label Classifiers under Label NoiseAuthors: Himanshu Kumar,Naresh Manwani,Sastry P S Conference: ACM India Joint International Conference on Data Science & Management of Data (CODS-COMAD-2020 2020) Date: 2020-01-05 Report no: IIIT/TR/2020/3 AbstractIn this paper, we address the problem of robust learning of multilabel classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and couple of surrogates of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feed-forward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on robust learning of multi-label classifiers under label noise. Full paper: pdf Centre for Visual Information Technology |
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