An efficient method for estimating the probability of misclassification applied to a problem in medical diagnosis

The problem of estimating the performance of a given classifier on a given data set is discussed for the case when no knowledge is available concerning the underlying distributions. A new method of estimating the probability of misclassification is proposed which yields essentially unbiased results similar to Lachenbruch's U-method with far less computation involved. While no theoretical work is presented, a practical rule of thumb is given for choosing the parameters of the estimator. The results are based on experiments performed with a data set concerning six diseases related to epigastric pain, and underline the importance of reporting performance on both the testing data and the training data. Whereas previous papers have continually reported results with a probability of correct classification as high as 74.3 per cent on the raw data and 92.0 per cent on “processed” data, in this paper it is shown that a much more significant estimate of the probability of correct classification based on this data set is 51.0 per cent.     

The relationship between the mass spectra of drugs and their biological activity—An application of artificial intelligence to chemistry

The mass spectra of a set of drugs consisting of sedatives and tranquilizers are analyzed and differentiated by pattern recognition methods, such as nonlinear mapping, K-nearest neighbors and Fisher discriminant. The mass spectral peaks are ranked, selected and weighed by the Fisher Ratio of each peak. The results indicate that it is possible to separate the two types of drugs and to predict the pharmacologic activity of “test” drugs with a high degree of accuracy. These general computer methods can be applied to other drugs or any data which requires separation into two classes.