Experimental Comparison of Methods for Multi label Classification in different Application Domains
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 examplebased 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.