Abstract
Distinguishing good chemical enhancers of percutaneous absorption from poor enhancers is a difficult problem. Previously, discriminant analysis and other machine learning methods have been applied to this problem. Results showed that the ordinary SVM provided the best result. In this work, we apply both SVM with different cost errors and sampling methods to improve the accuracy of classification. We show that a good classification is possible.
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Shah, A., Moss, G.P., Sun, Y., Adams, R., Davey, N., Wilkinson, S. (2012). Using a Support Vector Machine and Sampling to Classify Compounds as Potential Transdermal Enhancers. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_62
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DOI: https://doi.org/10.1007/978-3-642-33266-1_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33265-4
Online ISBN: 978-3-642-33266-1
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