An iterative oversampling approach for ordinal classification
Francisco Batel Marques, Hugo Duarte, João A.M. Santos, Inês Domingues, José Pereira Amorim, Pedro Henriques Abreu · 2019
The machine learning field has grown considerably in the last years. There are, however, some problems still to be solved. The characteristics of the training sets, for instance, are known to affect the classifiers performance. Here, and inspired by medical applications, we are interested in studying datasets that are both ordinal and imbalanced. Ordinal datasets present labels where only the relative ordering between different values is significant. Imbalanced datasets have very different quantity of examples per class.