Binary classification with ambiguous training data

• URL: http://arxiv.org/abs/2011.02598v1
• Date: Thu, 5 Nov 2020 00:53:58 GMT
• Title: Binary classification with ambiguous training data
• Authors: Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama
• Abstract summary: In supervised learning, we often face with ambiguous (A) samples that are difficult to label even by domain experts. This problem is substantially different from semi-supervised learning since unlabeled samples are not necessarily difficult samples.
• Score: 69.50862982117127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In supervised learning, we often face with ambiguous (A) samples that are difficult to label even by domain experts. In this paper, we consider a binary classification problem in the presence of such A samples. This problem is substantially different from semi-supervised learning since unlabeled samples are not necessarily difficult samples. Also, it is different from 3-class classification with the positive (P), negative (N), and A classes since we do not want to classify test samples into the A class. Our proposed method extends binary classification with reject option, which trains a classifier and a rejector simultaneously using P and N samples based on the 0-1-$c$ loss with rejection cost $c$. More specifically, we propose to train a classifier and a rejector under the 0-1-$c$-$d$ loss using P, N, and A samples, where $d$ is the misclassification penalty for ambiguous samples. In our practical implementation, we use a convex upper bound of the 0-1-$c$-$d$ loss for computational tractability. Numerical experiments demonstrate that our method can successfully utilize the additional information brought by such A training data.

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