This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.

Mazzatorta, P., Benfenati, E., Lorenzini, P., Vighi, M. (2004). QSAR in ecotoxicity: an overview of modern classification techniques. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 44(1), 105-112 [10.1021/ci034193w].

QSAR in ecotoxicity: an overview of modern classification techniques

VIGHI, MARCO
2004

Abstract

This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.
Articolo in rivista - Articolo scientifico
QSAR, ecotoxicology
English
2004
44
1
105
112
none
Mazzatorta, P., Benfenati, E., Lorenzini, P., Vighi, M. (2004). QSAR in ecotoxicity: an overview of modern classification techniques. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 44(1), 105-112 [10.1021/ci034193w].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/895
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