Indices for the evaluation of neural network performance as classifier: Application to structural elucidation in infrared spectroscopy

Autor:	Daniel Cabrol-Bass, Claude Cachet, Thomas P. Forrest, Nicolas Sbirrazzuoli
Rok vydání:	1993
Předmět:	Network architecture Artificial neural network business.industry Computer science Feature extraction Machine learning computer.software_genre Artificial Intelligence Network performance Artificial intelligence business Classifier (UML) computer Software
Zdroj:	Neural Computing & Applications. 1:229-239
ISSN:	1433-3058 0941-0643
DOI:	10.1007/bf02098740
Popis:	An application of Artificial Neural Networks (ANN) to the substructure detection, from infrared spectra, of organic compounds is described. Several ANNs have already been implemented for this purpose, and show promising initial results; however, many problems remain to be resolved. We wished to train ANNs to assist in a decision support system using several spectroscopic methods to elucidate the structure of unknown molecules. To optimise the ANN with respect to spectral feature extraction, network architecture, training regime and threshold determination, we have investigated several indices for use in the evaluation of network performance. Since much published work on ANN application in this field present performance indices that are poorly defined or of limited use, we recommend that the basic results be reported so that readers may calculate indices to suit their own particular needs. These basic quantities are identified and a set of derived indices recommended.
Databáze:	OpenAIRE
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