Batch and online learning algorithms for nonconvex neyman-pearson classification
Batch and online learning algorithms for nonconvex neyman-pearson classification
复制标题
DOI：
10.1145/1961189.1961200
复制
发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
G. Gasso;A. Pappaioannou;Marina Spivak;L. Bottou
中科院分区：
文献类型：
--
作者：
G. Gasso;A. Pappaioannou;Marina Spivak;L. Bottou
我们描述并评估了两种用于 Neyman-Pearson (NP) 分类问题的算法，该算法最近被证明对于二分排序问题特别重要。 NP 分类是一个涉及假阴性率约束的非凸问题。我们研究了基于 DC 编程和随机梯度法的批量算法，非常适合大规模数据集。经验证据说明了所提出方法的潜力。
We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large-scale datasets. Empirical evidences illustrate the potential of the proposed methods.