Constrained submodular minimization for missing labels and class imbalance in multi-label learning

Abstract
In multi-label learning, there are two main challenges: missing labels and class imbalance (CIB). The former assumes that only a partial set of labels are provided for each training instance while other labels are missing. CIB is observed from two perspectives: first, the number of negative labels of each instance is much larger than its positive labels; second, the rate of positive instances (i.e. the number of positive instances divided by the total number of instances) of different classes are significantly different. Both missing labels and CIB lead to significant performance degradation. In this work, we propose a new method to handle these two challenges simultaneously. We formulate the problem as a constrained submodular minimization that is composed of a submodular objective function that encourages label consistency and smoothness, as well as, class cardinality bound constraints to handle class imbalance. We further present a convex approximation based on the Lovasz extension of submodular functions, leading to a linear program, which can be efficiently solved by the alternative direction method of multipliers (ADMM). Experimental results on several benchmark datasets demonstrate the improved performance of our method over several state-of-the-art methods.

Acknowledgements
This work is supported supported by competitive research funding from King Abdullah University of Science and Technology (KAUST). The participation of Siwei Lyu in this work is partly supported by US National Science Foundation Research Grant (CCF-1319800) and National Science Foundation Early Faculty Career Development (CAREER) Award (IIS-0953373). We thank reviewers for their constructive comments.

Publisher
AAAI press

Conference/Event Name
30th AAAI Conference on Artificial Intelligence, AAAI 2016

Additional Links
https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11762

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