Variable selection and neural networks

This book focuses particularly on the application of chemometrics in the field of analytical chemistry. In infrared spectroscopy for instance, chemometrics consists in the prediction of a quantitative variable (the obtention of which is delicate, requiring a chemical analysis and a qualified operator), such as the concentration of a component present in the studied product from spectral data measured on various wavelengths or wavenumbers. In this book the author proposes a methodology in the field of chemometrics to handle the spectrophotometric data which are often represented in high dimension. To handle these data, a new incremental method (step-by-step) is proposed for the selection of spectral data using linear and non-linear regression. The author proposes, also, to improve the previous method by a judicious choice of the first selected variable, which has a very important influence on the final performances of the prediction. The idea is to use a measure of the mutual information between the independent and dependent variables to select the first one; then the previous incremental method (step-by-step) is used to select the next variables.

Autorentext

Nabil Benoudjit received the Ph.D. degree in electrical engineering from the Université catholique de Louvain (Belgium) in 2003. He is now a Lecturer at the University of Batna (Algeria). He teaches pattern recognition, signal processing and artificial neural networks. His research interests are in the area of high-dimensional data analysis.