Improved Discriminant Training and Feature Extraction for the Generation of Simulated Mass Spectra of Small Organic Molecules

Abstract

An empirical method employing computerized pattern recognition techniques has been applied previously to the generation of simulated mass spectra of small organic molecules. The techniques have been improved in two ways to yield superior performance. First, a method for training adaptive binary pattern classifiers using an iterative least squares approach is used. Second, a feature extraction technique known as an attribute inclusion algorithm is used to investigate the importance of multiple features in the molecular descriptions.