10.5120/20083-1666 |
Passent El Kafrawy, Amr Mausad and Heba Esmail. Article: Experimental Comparison of Methods for Multi-label Classification in different Application Domains. International Journal of Computer Applications 114(19):1-9, March 2015. Full text available. BibTeX
@article{key:article, author = {Passent El Kafrawy and Amr Mausad and Heba Esmail}, title = {Article: Experimental Comparison of Methods for Multi-label Classification in different Application Domains}, journal = {International Journal of Computer Applications}, year = {2015}, volume = {114}, number = {19}, pages = {1-9}, month = {March}, note = {Full text available} }
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.