2016
DOI: 10.1002/cae.21743
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Comparative evaluation of four multi‐label classification algorithms in classifying learning objects

Abstract: With 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… Show more

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Cited by 13 publications
(3 citation statements)
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References 25 publications
(46 reference statements)
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“…Support vector machine (SVM), Knearest neighbors (KNN), and artificial neural network (ANN) and decision tree are different types of classification techniques. 14 In this work, SVM was used.…”
Section: Analysis Of Ft-infrared Resultsmentioning
confidence: 99%
“…Support vector machine (SVM), Knearest neighbors (KNN), and artificial neural network (ANN) and decision tree are different types of classification techniques. 14 In this work, SVM was used.…”
Section: Analysis Of Ft-infrared Resultsmentioning
confidence: 99%
“…The procedure follows a simple way to cluster a given data set through a center number of groups (assume k clusters) fixed apriori. Classification model: Support vector machine . With the increasing number of enrollments, the demand to identify, analyze, and classify the activities in MOOCs has arisen and become a critical issue . Some classical classification models in educational domains include support vector machine, neural networks, decision trees, logistic regression, and random forest.…”
Section: Methodsmentioning
confidence: 99%
“…More specifically, the model provides a methodology that shows the task of multi-label mapping of LOs into different kinds of inquiries. In Aldrees & Chikh [22], researchers identify learning items by comparing and contrasting four multi-label classification systems. Carrillo et al [23] investigate hierarchical multi-label categorization in the context of recommender systems.…”
Section: -2-classification Of Learning Objectmentioning
confidence: 99%