Comparative evaluation of four multi-label classification algorithms in classifying learning objects

The classification of learning objects (LOs) enables users to search for, access, and reuse them as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel learning approach is represented for classifying and ranking multi-labelled LOs, whereas each LO might be associated with multiple labels as opposed to a single-label approach. A comprehensive overview of the common fundamental multi-label classification algorithms and metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and extracted from ARIADNE Learning Object Repository. We experimentally train four effective multi-label classifiers on the created LOs dataset and then, assess their performance based on the results of 16 evaluation metrics. The result of this article will answer the question of: what is the best multi-label classification algorithm for classifying multi-labelled LOs?