| Abstract: | In recent years numerous methods of pattern recognition have been tested for automatic interpretation of physicochemical data. Classifiers have been used successfully, especially with low resolution mass spectra. However, judgement of spectral classifiers (‘percentage of correctly classified spectra’) was often mathematically insufficiently defined. In this paper basic principles of the probability theory and information theory are used to derive objective criteria for binary classifiers. A classifier is an algorithm that uses a pattern vector (mass spectrum) and a priori probabilities for the classes (chemical structures) to which this vector belongs; the classification results are a posteriori probabilities for the classes. Predictive abilities for both classes or the information gain are suitable, objective criteria, to compare classifiers. Mathematical formulae are given and explained by examples from mass spectrometry. |
