Efficient F Measure Maximization via Weighted Maximum Likelihood
Abstract
The classification models obtained via maximum likelihood-based training do not necessarily reach the optimal $F_\beta $ F β -measure for some user’s choice of $\beta $ β that is achievable with the chosen parametrization. In this work we link the weighted maximum entropy and the optimization of the expected $F_\beta $ F β -measure, by viewing them in the framework of a general common multi-criteria optimization problem. As a result, each solution of the expected $F_\beta $ F β -measure maximization can be realized as a weighted maximum likelihood solution within the maximum entropy model - a well understood and behaved problem for which standard (off the shelf) gradient methods can be used. Based on this insight, we present an efficient algorithm for optimization of the expected $F_\beta $ F β using weighted maximum likelihood with dynamically adaptive weights.
Cite
Text
Dimitroff et al. "Efficient F Measure Maximization via Weighted Maximum Likelihood." Machine Learning, 2015. doi:10.1007/S10994-014-5439-YMarkdown
[Dimitroff et al. "Efficient F Measure Maximization via Weighted Maximum Likelihood." Machine Learning, 2015.](https://mlanthology.org/mlj/2015/dimitroff2015mlj-efficient/) doi:10.1007/S10994-014-5439-YBibTeX
@article{dimitroff2015mlj-efficient,
title = {{Efficient F Measure Maximization via Weighted Maximum Likelihood}},
author = {Dimitroff, Georgi and Georgiev, Georgi and Tolosi, Laura and Popov, Borislav},
journal = {Machine Learning},
year = {2015},
pages = {435-454},
doi = {10.1007/S10994-014-5439-Y},
volume = {98},
url = {https://mlanthology.org/mlj/2015/dimitroff2015mlj-efficient/}
}