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

Abstract

ABSTRACTWith the increasing number of learning objects (LOs), the possibility of their fast and effective retrieving and storing has become a more critical issue. The classification of LOs enables users to search for, access, and reuse them in an effective and efficient way. In this article, the multi‐label learning approach is represented for classifying and ranking multi‐labeled 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‐labeled 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‐labeled LOs? © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:651–660, 2016; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21743