Research on loss function for adaptive label thresholding algorithm
Hongcheng Tang, Tingting Zhai · 2022
The loss function plays a key role in the performance of online multi-label classification algorithms. Based on the design idea of the multi-label classification hinge loss function in the adaptive label thresholding algorithm, this paper expands several binary classification loss functions to new multi-label classification loss functions, proposes several adaptive label thresholding algorithms based on these new loss functions, and investigate the impact of different loss functions on multilabel classification performance. The experimental results show that the adaptive label thresholding algorithm based on the logistic loss function achieves the best performance, and the adaptive label thresholding algorithms using different loss functions are all better than several advanced comparison algorithms.