Ranking the Best Instances
Ranking the Best Instances
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DOI：
10.5555/1314498.1390330
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发表时间：
2006-11
期刊：
影响因子：
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通讯作者：
S. Clémençon;N. Vayatis
中科院分区：
文献类型：
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作者：
S. Clémençon;N. Vayatis
我们制定了一个本地形式的二分排名问题的目标是集中在最好的情况下。我们提出了一种方法的基础上建设的实值评分功能。我们研究了涉及分数经验分位数的专用统计数据的经验风险最小化。我们首先说明的问题，找到最好的例子，可以作为一个分类问题与质量约束。接下来，我们开发了特殊的性能指标的局部排名问题，扩展了ROC曲线（AUC）标准下的面积，并描述了这些新的标准的最佳元素。我们还强调了这样一个事实，即对最佳实例进行排名的目标不能以阶段方式实现，首先，最佳实例将被暂时识别，然后可以应用标准AUC标准。最后，我们列出了局部排名问题的初步统计结果。
We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stage-wise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.