A boosting method for maximization of the area under the ROC curve
A boosting method for maximization of the area under the ROC curve
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DOI：
10.1007/s10463-009-0264-y
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发表时间：
2011-10-01
影响因子：
1
通讯作者：
Komori, Osamu
中科院分区：
文献类型：
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作者：
Komori, Osamu
本文讨论了临床领域中二元分类问题的受试者工作特征曲线（ROC）和ROC曲线下面积（AUC）。我们提出了一种统计方法，结合多个特征变量，基于一个升压算法的AUC最大化。在这种迭代过程中，由特征变量组成的各种简单分类器被灵活地组合成一个强分类器。我们考虑正则化，以防止过度拟合的数据在算法中使用的惩罚项的非光滑性。这种正则化方法不仅提高了分类性能，而且有助于我们更清楚地了解每个特征变量与二元结果变量的关系。我们证明了有用的分数图构建组件的助推方法。我们描述了两个模拟研究和一个真实的数据分析，以说明我们的方法的效用。
We discuss receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) for binary classification problems in clinical fields. We propose a statistical method for combining multiple feature variables, based on a boosting algorithm for maximization of the AUC. In this iterative procedure, various simple classifiers that consist of the feature variables are combined flexibly into a single strong classifier. We consider a regularization to prevent overfitting to data in the algorithm using a penalty term for nonsmoothness. This regularization method not only improves the classification performance but also helps us to get a clearer understanding about how each feature variable is related to the binary outcome variable. We demonstrate the usefulness of score plots constructed componentwise by the boosting method. We describe two simulation studies and a real data analysis in order to illustrate the utility of our method.