Exploiting Meta-Learned Confidences for Imbalanced Multilabel Learning

Multilabel learning deals with datasets where each sample is associated with multiple labels. It is commonly assumed that label correlations should be well exploited to build an effective multilabel classifier. Moreover, the class imbalance problem occurs in many multilabel datasets and should be tackled to reduce the classification bias. While many multilabel learning methods have been proposed, research on imbalanced multilabel learning (IMLL) is relatively deficient. To address these issues, we exploit the value of meta-learned confidences, i.e., the prediction confidences iteratively updated over the out-of-bag samples, for IMLL. First, such meta-confidences can be fused to the original feature space to learn high-order label correlations. Second, meta-confidences can be used to calibrate the prediction results to alleviate class imbalance. Motivated by these, we propose an ensemble learning method named meta-confidence ensemble (MCE) for IMLL. Specifically, MCE iteratively makes bootstrap replicates of the multilabel training set, leverages the out-of-bag samples to generate meta-confidences, and fuses them to the original feature space to learn label correlations. A sparse projection method is presented to avoid overfitting and improve the ensemble diversity. Finally, the prediction result of an unseen sample is determined by the calibrated plurality vote of MCE's base classifiers. Extensive experiments demonstrated the effectiveness and superiority of MCE for IMLL. Codes have been made publicly available at https://github.com/ CUHKSZ-NING/MCEClassifier.