Synthetic Oversampling of Multi-label Data Based on Local Label Distribution

B. Liu, G. Tsoumakas, (2019) Synthetic Oversampling of Multi-label Data Based on Local Label Distribution, 2019 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019), Würzburg, Germany.

Multi-label learning, Class-imbalance, Synthetic oversampling, Local label distribution, Ensemble methods

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multi-label sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare sub-concepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.