Speeding Up Classifier Chains in Multi-label Classification
Jose M. Moyano, Eva Gibaja, Sebastián Ventura, Alberto Cano · 2019
Multi-label classification has attracted increasing attention of the scientific community in recent years, given its ability to solve problems where each of the examples simultaneously belongs to multiple labels. From all the techniques developed to solve multi-label classification problems, Classifier Chains has been demonstrated to be one of the best performing techniques. However, one of its main drawbacks is its inherently sequential definition. Although many research works aimed to reduce the runtime of multi-label classification algorithms, to the best of our knowledge, there are no proposals to specifically reduce the runtime of Classifier Chains. Therefore, in this paper we propose a method called Parallel Classifier Chains which enables the parallelization of Classifier Chain. In this way, Parallel Classifier Chains builds k binary classifiers in parallel, where each of them includes as extra input features the predictions of those labels that have been previously built. We performed an experimental evaluation over 20 datasets using 5 metrics to analyze both the runtime and the predictive performance of our proposal. The results of the experiments affirmed that our proposal was able to significantly reduce the runtime of Classifier Chains while the predictive performance was not statistically significantly harmed.