Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of robust learning of multi-label 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 a surrogate of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on the robust learning of multi-label classifiers under label noise. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3371158.3371169 | PROCEEDINGS OF THE 7TH ACM IKDD CODS AND 25TH COMAD (CODS-COMAD 2020) |
Keywords | Field | DocType |
multi-label, neural networks, label noise, robust losses | Pattern recognition,Computer science,Robust learning,Artificial intelligence | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Himanshu Kumar | 1 | 27 | 6.10 |
| Naresh Manwani | 2 | 0 | 1.01 |
| P. Sastry | 3 | 235 | 12.27 |