Experimental Comparison of Methods for Multi-label Classification in different Application Domains
Passent Elkafrawy, Amr M. Sauber, Heba Esmail · International Journal of Computer Applications · 2015
Real-world applications have begun to adopt the multi-label paradigm.The multi-label classification implies an extra dimension because each example might be associated with multiple labels (different possible classes), as opposed to a single class or label (binary, multi-class) classification.And with increasing number of possible multi-label applications in most ecosystems, there is little effort in comparing the different multi-label methods in different domains.Hence, there is need for a comprehensive overview of methods and metrics.In this study, we experimentally evaluate 11 methods for multi-label learning using 6 evaluation measures over seven benchmark datasets.The results of the experimental comparison revealed that the best performing method for both the example-based evaluation measures and the label-based evaluation measures are ECC on all measures when using C4.5 tree classifier as a single-label base learner.