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Published 2008-09-03. [This article argues that cross validation and resampling/bootstrapping cannot work well So-called intelligent computer-based methods for classifying patient samples, for example, have been evaluated with the help of two methods that have completely dominated research for 25 years. Now Swedish researchers at Uppsala University are revealing that this methodology is worthless when it comes to practical problems. The article is published in the journal Pattern Recognition Letters.

Today there is rapidly growing interest in 'intelligent' computer-based methods that use various classes of measurement signals, from different patient samples, for instance, to create a model for classifying new observations. This type of method is the basis for many technical applications, such as recognition of human speech, images, and fingerprints, and is now also beginning to attract new fields such as health care.

To evaluate the performance of a classification model, one normally tests it on a number of trial examples that have never been involved in the design of the model. Unfortunately there are seldom tens of thousands of test examples available for this type of evaluation. In biomedicine, for instance, it is often expensive and difficult to collect the patient samples needed, especially if one wishes to analyze a rare disease. To solve this problem, many different methods have been proposed. Since the 1980s two methods have completely dominated research, namely, cross validation and resampling/bootstrapping.

"Our main conclusion is that this methodology cannot be depended on at all, and that it therefore needs to be immediately replaces by Bayesian methods, for example, which can deliver reliable measures of the uncertainty that exists. Only then will multivariate analyses be in any position to be adopted in such critical applications as health care," says Mats Gustafsson.

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