| Texto completo | |
| Autor(es): |
Donyavi, Zahra
;
Li, Feiyu
;
Zhang, Yunrui
;
Silva, Diego F.
;
Batista, Gustavo E.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025; v. N/A, p. 10-pg., 2025-01-01. |
| Resumo | |
Machine learning models often suffer from performance degradation when dealing with class distributions that differ from the training distribution, a scenario commonly referred to as label shift. Addressing this challenge, this paper introduces Match, a novel adjustment approach that maximizes the likelihood of predicted probabilities under class prevalence constraints. Unlike existing methods such as retraining with instance re-weighting and the Bayes update rule, Match ensures that the adjusted class distribution aligns precisely with the prevalence estimates from quantifiers. By formulating the adjustment process as a binary integer linear optimization problem, Match benefits from efficient mixed-integer solvers. Extensive experiments demonstrate that Match outperforms the state-of-the-art in classifier adjustment with statistical significance, particularly in handling scenarios with imbalanced distributions. (AU) | |
| Processo FAPESP: | 22/03176-1 - Aprendizado de máquina para séries temporais em aplicações de mHealth |
| Beneficiário: | Diego Furtado Silva |
| Modalidade de apoio: | Auxílio à Pesquisa - Projeto Inicial |