Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach Versus Structured Loss Minimization
Abstract
We compare the plug-in rule approach for optimizing the F-measure in multi-label classification with an approach based on structured loss minimization, such as the structured support vector machine (SSVM). Whereas the former derives an optimal prediction from a probabilistic model in a separate inference step, the latter seeks to optimize the F-measure directly during the training phase. We introduce a novel plug-in rule algorithm that estimates all parameters required for a Bayes-optimal prediction via a set of multinomial regression models, and we compare this algorithm with SSVMs in terms of computational complexity and statistical consistency. As a main theoretical result, we show that our plug-in rule algorithm is consistent, whereas the SSVM approaches are not. Finally, we present results of a large experimental study showing the benefits of the introduced algorithm.
Cite
Text
Dembczynski et al. "Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach Versus Structured Loss Minimization." International Conference on Machine Learning, 2013.Markdown
[Dembczynski et al. "Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach Versus Structured Loss Minimization." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/dembczynski2013icml-optimizing/)BibTeX
@inproceedings{dembczynski2013icml-optimizing,
title = {{Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach Versus Structured Loss Minimization}},
author = {Dembczynski, Krzysztof and Jachnik, Arkadiusz and Kotlowski, Wojciech and Waegeman, Willem and Huellermeier, Eyke},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1130-1138},
volume = {28},
url = {https://mlanthology.org/icml/2013/dembczynski2013icml-optimizing/}
}