Many-Objective Ensemble-Based Multilabel Classification

  • Marcos M. Raimundo
  • Fernando J. Von Zuben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


This paper proposes a many-objective ensemble-based algorithm to explore the relations among the labels on multilabel classification problems. This proposal consists in two phases. In the first one, a many-objective optimization method generates a set of candidate components exploring the relations among the labels, and the second one uses a stacking method to aggregate the components for each label. By balancing or not the relevance of each label, two versions were conceived for the proposal. The balanced one presented a good performance for recall and F1 metrics, and the unbalanced one for 1-Hamming loss and precision metrics.


Multilabel classification Many-objective optimization Multiobjective optimization Ensemble of classifiers Stacking 



This research was supported by grants from FAPESP, process #14/13533-0, and CNPq, process #309115/2014-0.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LBiC/DCA/FEEC - University of CampinasCampinasBrazil

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