  So far, multi-label classification algorithms have been evaluated using
statistical methods that do not consider the semantics of the considered
classes and that fully depend on abstract computations such as Bayesian
Reasoning. Currently, there are several attempts to develop ontology-based
methods for a better assessment of supervised classification algorithms. In
this research paper, we define a novel approach that aligns expected labels
with predicted labels in multi-label classification using ontology-driven
feature-based semantic similarity measures and we use it to develop a method
for creating precise confusion matrices for a more effective evaluation of
multi-label classification algorithms.
