Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Local Irritation/Corrosion Testing Strategies: Extending a Decision Support System by Applying Self-Learning Classifiers

Local Irritation/Corrosion Testing Strategies: Extending a Decision Support System by Applying... Procedures have been established and tested for the extension of a decision support system (DSS) for the prediction of the local irritation/corrosion potential of chemicals by using self-learning classifiers. The different approaches (decision trees, distances examinations in a multidimensional space, k-nearest neighbour method) have been implemented, tested and evaluated independently. A combination of all of the established extension approaches was also developed and tested. Self-learning classifiers are constructed “automatically” by a computer, i.e. they are not derived by a human expert, and thus they can be constructed with minimal effort. The classifiers presented here extend the existing DSS in a manner that increased significantly the predictive power of the extended system. However, automatically calculated results of self-learning classifiers are produced by a machine, and a machine is incapable of explaining the toxicological relevance of the results obtained. Thus, these results must be accepted, despite an inability to prove their reliability. Only the mathematical correctness of the method and the prediction rates for suitable test cases can lend some credibility to predictions produced by a computer calculating on a self-learning basis. This may not be adequate for regulatory hazard assessment purposes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Alternatives to Laboratory Animals SAGE

Local Irritation/Corrosion Testing Strategies: Extending a Decision Support System by Applying Self-Learning Classifiers

Download PDF
13 pages

Loading next page...
 
/lp/sage/local-irritation-corrosion-testing-strategies-extending-a-decision-U1N0DIL7mi

References (9)

Publisher
SAGE
Copyright
© 2000 Fund for the Replacement of Animals in Medical Experiments
ISSN
0261-1929
eISSN
2632-3559
DOI
10.1177/026119290002800507
Publisher site
See Article on Publisher Site

Abstract

Procedures have been established and tested for the extension of a decision support system (DSS) for the prediction of the local irritation/corrosion potential of chemicals by using self-learning classifiers. The different approaches (decision trees, distances examinations in a multidimensional space, k-nearest neighbour method) have been implemented, tested and evaluated independently. A combination of all of the established extension approaches was also developed and tested. Self-learning classifiers are constructed “automatically” by a computer, i.e. they are not derived by a human expert, and thus they can be constructed with minimal effort. The classifiers presented here extend the existing DSS in a manner that increased significantly the predictive power of the extended system. However, automatically calculated results of self-learning classifiers are produced by a machine, and a machine is incapable of explaining the toxicological relevance of the results obtained. Thus, these results must be accepted, despite an inability to prove their reliability. Only the mathematical correctness of the method and the prediction rates for suitable test cases can lend some credibility to predictions produced by a computer calculating on a self-learning basis. This may not be adequate for regulatory hazard assessment purposes.

Journal

Alternatives to Laboratory Animals – SAGE

Published: Sep 1, 2000

There are no references for this article.