Dealing with Imbalanceness in Hierarchical Classification Problems Through Data Resampling

Many important classification problems are imbalanced. Although resampling approaches are a common solution for different types of classification problems, they were still not defined for hierarchical classification problems. The objective of this work is to propose novel resampling approaches to handle the class imbalanceness issue in hierarchical classification problems. Four directions were investigated: (i) The use of classic resampling methods; (ii) A label path conversion strategy; (iii) The design of schemas to use resampling algorithms with local approaches; (iv) The proposal of global resampling algorithms. To show the impacts of the contribution of this work, we have investigated the imbalanceness issue in the COVID-19 identification in chest x-ray images.