AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification

Abstract

Automated Machine Learning (AutoML) has achieved high popularity in recent years. However, most of these studies have investigated alternatives to single-label classification problems, presenting a need for more investigations in the multi-label classification scenario. From the AutoML point of view, the few studies on multi-label classification focus on automatically finding the best models based on mono-objective optimization. These tools train several multi-label classifiers in search of the one with the best performance in a single objective optimization process. In this work, we propose AutoMMLC, a new multi-objective AutoML method for multi-label classification, to find the best models that maximize the f-score measure and minimize the training time. Experiments were carried out with ten multi-label datasets and different versions of the proposed method using two multi-objective optimization algorithms: Multi-objective Random Search and Non-Dominated Sorting Genetic Algorithm II. We evaluated the Pareto front obtained by these methods through the hypervolume metric. The Wilcoxon test demonstrated that AutoMMLC versions had similar results for this metric. Multi-label Classification (MLC) algorithms were obtained from the Pareto frontiers through the Frugality Score and compared with the baseline algorithms. The Friedman test demonstrated that the MLC algorithms from AutoMMLC versions had equal performances to f-score and training time. Furthermore, they had better results than baseline algorithms for f-score and better results than most baseline algorithms for training time.