论文标题
使用其他现有方法评估非线性决策树以进行二进制分类任务
Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods
论文作者
论文摘要
将数据集分类为两个或多个不同的类是一项重要的机器学习任务。许多方法都能够在测试数据上具有很高的精度对二进制分类任务进行分类，但是不能为用户提供任何易于解释的解释，以使对数据将数据分为两类的原因有更深入的了解。在本文中，我们强调并评估了最近提出的非线性决策树方法，并在许多数据集中使用许多常用的分类方法，涉及一些至少的功能。该研究揭示了关键问题，例如分类对方法的参数值的影响，分类器的复杂性与实现的准确性以及所得分类器的解释性。
Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation for users to have a deeper understanding of reasons for the split of data into two classes. In this paper, we highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets involving a few to a large number of features. The study reveals key issues such as effect of classification on the method's parameter values, complexity of the classifier versus achieved accuracy, and interpretability of resulting classifiers.
