多类问题求解：综述与综述
Solving for multi-class: a survey and synthesis
摘要：
许多最好的统计分类算法是只能区分两个类别之一的二进制分类器。将二元分类推广到多类的可能方法的数量随着类的数量呈指数级增加。有迹象表明，最好的方法将取决于数据集。因此，我们对数据驱动的解决方案设计特别感兴趣，无论是基于先前的考虑还是基于对数据的实证检验。在这里，我们演示了如何使用递归控制语言来描述多类分类中的多种不同划分策略，包括最常用的划分策略。我们使用它来手动构造新的分区配置，以及检查那些自动设计的分区配置。八种不同的策略进行了测试，在八个不同的数据集使用支持向量机（SVM）作为基本的二元分类。数值结果表明，一个尺寸适合所有的解决方案，包括一对一是适当的大多数数据集。三个数据集使用不同的方法显示出更好的准确性。最佳解决方案的最大改善数据集利用数据的属性，产生的不确定系数36\%（0.016绝对增益）比一个-比。一个.对于相同的数据集，经验性地检查数据的自适应解决方案也比一对一更准确。一个是更快。
英文摘要：
Many of the best statistical classification algorithms are binary classifiers that can only distinguish between one of two classes. The number of possible ways of generalizing binary classification to multi-class increases exponentially with the number of classes. There is some indication that the best method will depend on the dataset. Hence, we are particularly interested in data-driven solution design, whether based on prior considerations or on empirical examination of the data. Here we demonstrate how a recursive control language can be used to describe a multitude of different partitioning strategies in multi-class classification, including those in most common use. We use it both to manually construct new partitioning configurations as well as to examine those that have been automatically designed. Eight different strategies were tested on eight different datasets using a support vector machine (SVM) as the base binary classifier. Numerical results suggest that a one-size-fits-all solution consisting of one-versus-one is appropriate for most datasets. Three datasets showed better accuracy using different methods. The best solution for the most improved dataset exploited a property of the data to produce an uncertainty coefficient 36\% higher (0.016 absolute gain) than one-vs.-one. For the same dataset, an adaptive solution that empirically examined the data was also more accurate than one-vs.-one while being faster.
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。
