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Splitting criteria for classification problems with multi-valued attributes and large number of classes

机译：具有多值属性和大量类的分类问题的划分标准

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摘要

Decision Trees and Random Forests are among the most popular methods for classification tasks. Two key issues faced by these methods are: how to select the best attribute to associate with a node and how to split the samples given the selected attribute. This paper addresses an important challenge that arises when nominal attributes with a large number of values are present: the computational time required to compute splits of good quality. We present a framework to generate computationally efficient splitting criteria that handle, with theoretical approximation guarantee, multi-valued nominal attributes for classification tasks with a large number of classes. Experiments with a number of datasets suggest that a method derived from our framework is competitive in terms of accuracy and speed with the Twoing criterion, one of few criteria available that is able to handle, with optimality guarantee, nominal attributes with a large number of distinct values. However, this method has the advantage of also efficiently handling datasets with a large number of classes. These experiments also give evidence of the potential of aggregating attributes to improve the classification power of decision trees. (C) 2018 Elsevier B.V. All rights reserved.
机译：决策树和随机森林是用于分类任务的最受欢迎的方法。这些方法面临的两个关键问题是：如何选择与节点关联的最佳属性，以及在给定所选属性的情况下如何拆分样本。本文解决了存在大量值的名义属性时出现的一项重要挑战：计算高质量分割所需的计算时间。我们提出了一个框架来生成计算效率高的拆分标准，该标准可在理论上近似保证的情况下处理具有大量类的分类任务的多值名义属性。对大量数据集进行的实验表明，从我们的框架中得出的一种方法在准确性和速度方面与Twoing准则具有竞争性，Twoing准则是能够以最优性保证处理具有大量不同特征的名义属性的少数可用准则之一价值观。但是，此方法的优点是还可以有效地处理具有大量类的数据集。这些实验还提供了聚集属性以提高决策树分类能力的潜力的证据。 （C）2018 Elsevier B.V.保留所有权利。

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