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USE OF ROUGH SETS AND SPECTRAL DATA FOR BUILDING PREDICTIVE MODELS OF REACTION RATE CONSTANTS
Collette, T.W. AND A. Szladow. USE OF ROUGH SETS AND SPECTRAL DATA FOR BUILDING PREDICTIVE MODELS OF REACTION RATE CONSTANTS. APPLIED SPECTROSCOPY. Society for Applied Spectroscopy, 48(11):1379-1386, (1994).
A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase min-infrared library spectra and a rule-building software system based on the mathematical theory of rough sets. A diverse set of 41 esters was used as training compounds. The model is an advance in the development of a generalized system for predicting environmentally important reactivity parameters based on spectroscopic data. By comparison to a previously developed model using the same training set with multiple linear regression (MLR), the rough-sets model provided better predictive power, was more widely applicable, and required less spectral data manipulation. [For the previous MLR model, a standard error of prediction (SEP) of 0.59 was calculated for 88% of the training set data under leave-one-out cross-validation. In the present study using rough sets, an SEP of 0.52 was calculated for 95% of the data set.] More importantly, analysis of the decision rules generated by rough-sets analysis can lead to a better understanding of both the reaction process under study and important trends in the spectral data, as well as underlying relationships between the two.