Framework for evaluation and analysis of distributed classification using several aggregation methods for binary and multiclass classification problems.
The results show that there is no aggregation method that performs better in every case. Its performance depends on the problem’s intrinsic characteristics. With this information, we were able to create a model that can be used as practical tool to guide the choice of the aggregation method for vertically partitioned machine learning problems.
Research project of Federal University of Rio Grande do Sul (UFRGS).
This project is headed by Mariana Recamonde Mendoza, PhD and was funded by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS)
Assistant researchers: