Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking for maximizing the AUC to learn from massive streams of class-imbalanced data. These methods suggest both linear and kernel-based bipartite ranking based on first and second-order online learning. Unlike kernelized ranker, linear ranker is more scalable learning algorithm. The existing linear online bipartite ranking frameworks lack either handling non-separable data or constructing adaptive large margin. In this work, we propose a linear online confidence-weighted bipartite ranking algorithm (CBR) that adopts soft confidence-weighted learning. The proposed algorithm leverages the same properties of soft confidence-weighted learning in a framework for bipartite ranking. We also propose a diagonal variation of the soft confidence-weighted algorithm to deal with high-dimensional data. We empirically evaluate the effectiveness of the proposed algorithms on several benchmark and high-dimensional datasets. The results validate the reliability of the proposed algorithms. The experimental results also show that our algorithms outperform or are at least comparable to the competing online AUC maximization methods.
View on arXiv