In many real world prediction problems, a classifier must, or should, assign more than one label to an instance, e.g. prediction of machine failures, musical genre classification, etc. For this kind of problem, multi-label classification methods are needed. One approach frequently used to learn multi-label predictors divides the problem into one or more multi-class classification problems, and combines the models constructed for each sub-problem to classify new instances with multiple labels. Although there are many multi-label learning methods, there is a need for exploring methods that can lead to improvement in prediction power. In this work, we propose and evaluate a new method, called RB (Random-Bagging), based on dataset transformation and combination of classifiers. Six real-world datasets were used to evaluate our method, which was compared to three existing methods. Results were considered promising.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.