Abstract
Multi-label learning has been becoming an increasingly active area into the machine learning community due to a wide variety of real world problems. However, only over the past few years class balancing for these kind of problems became a topic of interest. In this paper, we present a novel method named hyperparameter calibration to treat class imbalance in a multi-label problem, to this aim we develop an extensive analysis over four real-world databases and two own synthetic databases exhibiting different ratios of imbalance. The empirical analysis shows that the proposed method is able to improve the classification performance when it is combined with three of the most widely used strategies for treating multi-label classification problems.
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Notes
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Train and test sets for emotions, scene, yeast and cal500 databases were obtained from http://simidat.ujaen.es/~research/MLSMOTE/index.html#datasets.
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Giraldo-Forero, A.F., Cardona-Escobar, A.F., Castro-Ospina, A.E. (2018). Multi-label Learning by Hyperparameters Calibration for Treating Class Imbalance. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_27
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