Classifier technology and the illusion of progress
Classifier technology and the illusion of progress
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DOI：
10.1214/088342306000000060
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发表时间：
2006-02-01
影响因子：
5.7
通讯作者：
Hand, David J.
中科院分区：
文献类型：
--
作者：
Hand, David J.
已经开发了大量的工具用于监督分类，从早期的方法如线性判别分析到现代的发展，如神经网络和支持向量机。为了确定这些方法的相对优越性，进行了大量的比较研究。本文认为，这些比较往往没有考虑到真实的问题的重要方面，因此，更复杂的方法的明显优势可能是一种错觉。特别是，简单的方法通常产生几乎与更复杂的方法一样好的性能，在某种程度上，性能的差异可能被其他来源的不确定性所淹没，这些不确定性通常在经典的监督分类范式中不被考虑。
A great many tools have been developed for supervised classification, ranging from early methods such as linear discriminant analysis through to modern developments such as neural networks and support vector machines. A large number of comparative studies have been conducted in attempts to establish the relative superiority of these methods. This paper argues that these comparisons often fail to take into account important aspects of real problems, so that the apparent superiority of more sophisticated methods may be something of an illusion. In particular, simple methods typically yield performance almost as good as more sophisticated methods, to the extent that the difference in performance may be swamped by other sources of uncertainty that generally are not considered in the classical supervised classification paradigm.