An extended tuning method for cost-sensitive regression and forecasting
An extended tuning method for cost-sensitive regression and forecasting
复制标题
成本敏感回归和预测的扩展调整方法
DOI：
10.1016/j.dss.2011.01.003
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
G. Bansal
中科院分区：
文献类型：
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作者：
Huimin Zhao;Atish P. Sinha;G. Bansal
在许多现实世界的回归和预测问题中，过度预测和预测不足的错误会产生不同的后果并产生不对称的成本。此类问题需要使用成本敏感学习，它试图最小化预期的误预测成本，而不是最小化均方误差等简单度量。最近提出了一种事后调整正则回归模型的方法，以最小化不对称成本结构下的平均误预测成本。在本文中，我们基于该方法提出了一种用于成本敏感回归的扩展调整方法。先前的方法成为我们提出的方法的特例。我们将所提出的方法应用于贷款冲销预测，这是一个成本敏感的回归问题，在过去几年中对银行倒闭产生了影响。贷款冲销预测领域的实证评估表明，我们提出的方法可以进一步显着降低误预测成本。
In many real-world regression and forecasting problems, over-prediction and under-prediction errors have different consequences and incur asymmetric costs. Such problems entail the use of cost-sensitive learning, which attempts to minimize the expected misprediction cost, rather than minimize a simple measure such as mean squared error. A method has been proposed recently for tuning a regular regression model post hoc so as to minimize the average misprediction cost under an asymmetric cost structure. In this paper, we build upon that method and propose an extended tuning method for cost-sensitive regression. The previous method becomes a special case of the method we propose. We apply the proposed method to loan charge-off forecasting, a cost-sensitive regression problem that has had a bearing on bank failures over the last few years. Empirical evaluation in the loan charge-off forecasting domain demonstrates that the method we have proposed can further lower the misprediction cost significantly.