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Binary decision prediction probabilities and the accuracy of interpretation of mass spectra
Author(s) -
Adams R. A.,
Sedgwick R. D.
Publication year - 1974
Publication title -
organic mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 0030-493X
DOI - 10.1002/oms.1210090908
Subject(s) - interpretation (philosophy) , binary number , computer science , binary decision diagram , mass spectrum , artificial intelligence , machine learning , binary classification , data mining , algorithm , mass spectrometry , mathematics , support vector machine , chemistry , arithmetic , chromatography , programming language
Abstract The results of the applications of computer learning techniques to decision making in the evaluation of mass spectral data are used to propose a model in which molecular complexity is related to binary decision prediction probability. This model is tested on the results from a mass spectrum interpretation programme and it is proposed that the method could be used to compare the effectiveness of programmes developed from different basic concepts.