Calibrated k-labelsets for Ensemble Multi-label Classification

RAndom k-labELsets (RAkEL) is an effective ensemble multi-label classification (MLC) model where each base-classifier is trained on a small random subset of k labels. However, the model construction does not fully benefit from the diversity of the ensemble and the label probability estimates obtained with RAkEL are usually badly calibrated due to the problems raised by the imbalanced label representation. In this paper, we propose three practical solutions to overcome these drawbacks. One is to increase the diversity of the base classifiers in the ensemble. The second to smooth the label powerset probability estimates during the ensemble aggregation process, and the third to calibrate the label decision thresholds. Experimental results on various benchmark data sets indicate that the proposed approach outperforms significantly recent state-of-the-art MLC algorithms, including RAkEL and its variants.