An instance-weighting method to induce cost-sensitive trees
An instance-weighting method to induce cost-sensitive trees
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DOI：
10.1109/tkde.2002.1000348
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发表时间：
2002-05-01
影响因子：
8.9
通讯作者：
Ting, KM
中科院分区：
文献类型：
--
作者：
Ting, KM
我们引入了一种实例加权方法来生成代价敏感树。它是标准树归纳过程的推广，其中只有初始实例权重决定要归纳的树的类型——最小错误树或最小高代价错误树。我们证明，它可以很容易地适应现有的树学习算法。以前的研究没有足够的证据支持贪婪分治算法可以直接从训练数据中有效地诱导出真正的代价敏感树的观点。我们在本文中提供了这一实证证据。在两类数据集中，结合实例加权方法的算法在总误分类代价、高代价错误数量和树大小方面都优于原算法。实例加权方法比先前基于改变先验的方法更简单、更有效。
We introduce an instance-weighting method to induce cost-sensitive trees. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree to be induced-minimum error trees or minimum high cost error trees. We demonstrate that it can be easily adapted to an existing tree learning algorithm. Previous research provides insufficient evidence to support the idea that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in terms of total misclassification costs, the number of high cost errors, and tree size in two-class data sets. The instance-weighting method is simpler and more effective in implementation than a previous method based on altered priors.