Does It Pay to Optimize AUC?
نویسندگان
چکیده
The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises question of whether generally insignificant gains observed by previous studies are due inherent limitations or inadequate quality optimization. To better understand value optimizing AUC, we present efficient algorithm, namely AUC-opt, find provably optimal linear classifier in R2, which runs O(n+n- log n+n-) where n+ n- number positive negative samples respectively. Furthermore, it can be naturally extended Rd O(n+n-d-1 (n+n-)) recursively calling AUC-opt lower-dimensional spaces. We prove problem NP-complete when d not fixed, reducing from open hemisphere problem. Compared with other methods, experiments show that achieves statistically significant improvements between 17 40 R2 4 42 R3 50 t-SNE training datasets. However, generally, gain proves on most testing datasets compared best standard classifiers. Similar observations found nonlinear methods under real-world
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2023
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v37i9.26349