Title
Consistent algorithms for multi-label classification with macro-at-k metrics
Authors
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2024
Chapter type
chapter in monograph / paper
Publication language
english
Keywords
EN
- multi-label classification
- complex performance metrics
- macro-at-k
- extreme classification
Abstract
EN We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable into a sum of binary classification utilities applied separately to each label with an additional requirement of exactly k labels predicted for each instance. These "macro-at-k" metrics possess desired properties for extreme classification problems with long tail labels. Unfortunately, the at-k constraint couples the otherwise independent binary classification tasks, leading to a much more challenging optimization problem than standard macro-averages. We provide a statistical framework to study this problem, prove the existence and the form of the optimal classifier, and propose a statistically consistent and practical learning algorithm based on the Frank-Wolfe method. Interestingly, our main results concern even more general metrics being non-linear functions of label-wise confusion matrices. Empirical results provide evidence for the competitive performance of the proposed approach.
Date of online publication
16.01.2024
Open Access Mode
publisher's website
Open Access Text Version
final published version
Date of Open Access to the publication
at the time of publication
Ministry points / conference (CORE)
200
System created by Poznań University of Technology
and Poznan Supercomputing and Networking Center
Log in through eKonto to add to SIS