当训练数据开销较大时的学习：类分布对树归纳的影响
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
摘要：
对于大的、真实世界的归纳学习问题，由于与获取、准备和存储训练样本相关联的成本和/或与从它们学习相关联的计算成本，训练样本的数量通常必须受到限制。在这种情况下，一个实际重要性的问题是：如果只能选择n个训练样本，那么类应该以什么比例表示？在这篇文章中，我们帮助回答这个问题的分析，对于一个固定的训练集大小，类分布的训练数据和分类树的性能从这些数据之间的关系。我们研究了26个数据集，并为每个数据集确定最佳的学习类分布。自然发生的类分布一般表现良好，分类器的性能进行评估时，使用未分化的出错率（0/1损失）。然而，当使用ROC曲线下面积来评估分类器性能时，平衡分布表现良好。由于这些选择的类分布都不总是产生最佳性能的分类器，我们引入了一个对数据敏感的渐进式采样算法，用于根据与每个示例相关联的类选择训练样本。该算法的实证分析表明，产生的训练集的类分布产生良好的（接近最佳）分类性能的分类器。
英文摘要：
For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a budget-sensitive progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
