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• W3089248819 abstract "The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant. In such cases, each $d$ dimensional sample has only $k$ non-zero features with $k ll d$, and data arrives sequentially in a streaming form. Current online AUC optimization algorithms have high per-iteration cost $mathcal{O}(d)$ and usually produce non-sparse solutions in general, and hence are not suitable for handling the data challenge mentioned above. In this paper, we aim to directly optimize the AUC score for high-dimensional sparse datasets under online learning setting and propose a new algorithm, textsc{FTRL-AUC}. Our proposed algorithm can process data in an online fashion with a much cheaper per-iteration cost $mathcal{O}(k)$, making it amenable for high-dimensional sparse streaming data analysis. Our new algorithmic design critically depends on a novel reformulation of the U-statistics AUC objective function as the empirical saddle point reformulation, and the innovative introduction of the lazy update rule so that the per-iteration complexity is dramatically reduced from $mathcal{O}(d)$ to $mathcal{O}(k)$. Furthermore, textsc{FTRL-AUC} can inherently capture sparsity more effectively by applying a generalized Follow-The-Regularized-Leader (FTRL) framework. Experiments on real-world datasets demonstrate that textsc{FTRL-AUC} significantly improves both run time and model sparsity while achieving competitive AUC scores compared with the state-of-the-art methods. Comparison with the online learning method for logistic loss demonstrates that textsc{FTRL-AUC} achieves higher AUC scores especially when datasets are imbalanced." @default.
• W3089248819 created "2020-10-01" @default.
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• W3089248819 date "2020-09-22" @default.
• W3089248819 modified "2023-10-16" @default.
• W3089248819 title "Online AUC Optimization for Sparse High-Dimensional Datasets" @default.
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• W3089248819 doi "https://doi.org/10.48550/arxiv.2009.10867" @default.
• W3089248819 hasPublicationYear "2020" @default.
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