we fit a multiclass SVM classifier, with one-vs-all approach and linear kernel, as it is a good fit for general applicability. Hyper-parameters are selected by grid search on the train set, using an internal 5-fold cross validation.

We evaluate the effectiveness of each method by the balanced accuracy score, which is suitable to evaluate on either balanced and imbalanced datasets. The methods are compared by their balanced accuracy.

The descriptors compose rankers, which are employed to generate ranks in our late-fusion representation. Our method varies with respect to which rankers are used, whether visual or textual rankers, or even their combinations for the multimodal scenario are applied. Besides that, it also varies with respect to which embedding approach is adopted. We evaluate these aspects experimentally.

We model our solution as a rank-fusion approach, followed by an estimator based on rank-fusion vectors. This approach intends to validate our hypothesis that unsupervised graph-based rank-fusion approaches can lead to effective representation models for prediction tasks in general.

We adopt the same experimental evaluation for all datasets but ME17-DIRSM, that defines its own procedure. In this case, the task imposes three evaluation scenarios, as follows. In the first one, called “visual”, only visual data 