Joint optimization of scoring and thresholding models for online multi-label classification

Existing online multi-label classification works cannot well handle the online label thresholding problem and lack the regret analysis for their online algorithms.This paper proposes a novel framework of adaptive label thresholding algorithms for online multi-label classification, with the aim to overcome the drawbacks of existing methods.The key feature of our framework is that both scoring and thresholding models are included as important components of the online multi-label classifier and are incorporated into one online optimization problem.Further, in order to establish the relationship between scoring and thresholding models, a novel multi-label classification loss function is derived,