Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees
Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees
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DOI：
10.1109/iccv.2013.31
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发表时间：
2013-12
期刊：
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通讯作者：
Oisin Mac Aodha;G. Brostow
中科院分区：
文献类型：
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作者：
Oisin Mac Aodha;G. Brostow
典型的分类方法将类别标签视为不相交的。对于每个训练示例，假设只有一个类标签能够正确描述它，并且所有其他标签都同样糟糕。然而我们知道，在许多情况下，好标签和坏标签都过于简单化，从而损害了准确性。在依赖于示例的成本敏感学习领域，每个标签都是一个向量，表示数据点对每个类的亲和力。在测试时，我们的目标不是最小化错误分类率，而是最大化亲和力。我们为决策树提出了一种新颖的依赖于示例的成本敏感杂质度量。我们的实验表明，这种新的杂质测量方法提高了测试性能，同时仍然保留了标准分类树的快速测试时间。我们在三个计算机视觉问题（跟踪、描述符匹配和光流）上比较了我们的分类树方法和其他成本敏感方法，并展示了所有三个领域的改进。
Typical approaches to classification treat class labels as disjoint. For each training example, it is assumed that there is only one class label that correctly describes it, and that all other labels are equally bad. We know however, that good and bad labels are too simplistic in many scenarios, hurting accuracy. In the realm of example dependent cost-sensitive learning, each label is instead a vector representing a data point's affinity for each of the classes. At test time, our goal is not to minimize the misclassification rate, but to maximize that affinity. We propose a novel example dependent cost-sensitive impurity measure for decision trees. Our experiments show that this new impurity measure improves test performance while still retaining the fast test times of standard classification trees. We compare our approach to classification trees and other cost-sensitive methods on three computer vision problems, tracking, descriptor matching, and optical flow, and show improvements in all three domains.