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arXiv · 1902.01056

Online Multiclass Classification Based on Prediction Margin for Partial Feedback

Takuo Kaneko·Issei Sato·Masashi Sugiyama
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Abstract

We consider the problem of online multiclass classification with partial feedback, where an algorithm predicts a class for a new instance in each round and only receives its correctness. Although several methods have been developed for this problem, recent challenging real-world applications require further performance improvement. In this paper, we propose a novel online learning algorithm inspired by recent work on learning from complementary labels, where a complementary label indicates a class to which an instance does not belong. This allows us to handle partial feedback deterministically in a margin-based way, where the prediction margin has been recognized as a key to superior empirical performance. We provide a theoretical guarantee based on a cumulative loss bound and experimentally demonstrate that our method outperforms existing methods which are non-margin-based and stochastic.

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Takuo Kaneko, Issei Sato, Masashi Sugiyama. 2019-02-04. Online Multiclass Classification Based on Prediction Margin for Partial Feedback. https://arxiv.org/abs/1902.01056

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