RB: A New Method for Constructing Multi-Label Classifiers Based on Random Selection and Bagging
Patrícia Pachiega da Gama, Flávia Bernardini, Bianca Zadrozny · Learning and Nonlinear Models · 2013
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.