Stochastic Proximal Algorithms for AUC Maximization
Stochastic Proximal Algorithms for AUC Maximization
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发表时间：
2018-07
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通讯作者：
Michael Natole;Yiming Ying;Siwei Lyu
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作者：
Michael Natole;Yiming Ying;Siwei Lyu
随机优化算法（例如随机梯度下降 (SGD)）以低廉的每次迭代成本顺序更新模型，使其适合大规模数据分析。现有的研究大多集中在分类准确性上。然而，这些不能直接应用于最大化不平衡分类和二分排序中 ROC 曲线下面积（AUC）的重要问题。在本文中，我们开发了一种新颖的 AUC 最大化随机近端算法，称为 SPAM。与之前的文献相比，我们的算法SPAM适用于非光滑惩罚函数，对于强凸函数，在空间和每次迭代成本都是一个数据的情况下，达到了O(log t t)的收敛速度。
Stochastic optimization algorithms such as stochastic gradient descent (SGD) update the model sequentially with cheap per-iteration costs, making them amenable for large-scale data analysis. Most of the existing studies focus on the classification accuracy. However, these can not be directly applied to the important problems of maximizing the Area under the ROC curve (AUC) in imbalanced classification and bipartite ranking. In this paper, we develop a novel stochastic proximal algorithm for AUC maximization which is referred to as SPAM. Compared with the previous literature, our algorithm SPAM applies to a non-smooth penalty function, and achieves a convergence rate of O ( log t t ) for strongly convex functions while both space and per-iteration costs are of one datum.