Information Ranking Using Optimum-Path Forest
  The task of learning to rank has been widely studied by the machine learning
community, mainly due to its use and great importance in information retrieval,
data mining, and natural language processing. Therefore, ranking accurately and
learning to rank are crucial tasks. Context-Based Information Retrieval systems
have been of great importance to reduce the effort of finding relevant data.
Such systems have evolved by using machine learning techniques to improve their
results, but they are mainly dependent on user feedback. Although information
retrieval has been addressed in different works along with classifiers based on
Optimum-Path Forest (OPF), these have so far not been applied to the learning
to rank task. Therefore, the main contribution of this work is to evaluate
classifiers based on Optimum-Path Forest, in such a context. Experiments were
performed considering the image retrieval and ranking scenarios, and the
performance of OPF-based approaches was compared to the well-known SVM-Rank
pairwise technique and a baseline based on distance calculation. The
experiments showed competitive results concerning precision and outperformed
traditional techniques in terms of computational load.
