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Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates

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Abstract

Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature selection to improve the classification performance of standard feature selection algorithms evaluated for the prediction of P-gp inhibitors and substrates. Two well-known classification algorithms, decision trees and support vector machines, were used to classify the chemical compounds. The experimental results showed better performance for boosting feature selection with respect to the standard feature selection algorithms while maintaining the capability for feature reduction.

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Acknowledgements

This work was supported in part by Project TIN2015-66108-P of the Spanish Ministry of Science and Innovation.

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  1. Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain

    Gonzalo Cerruela García & Nicolás García-Pedrajas

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  1. Gonzalo Cerruela García
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  2. Nicolás García-Pedrajas
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Correspondence to Gonzalo Cerruela García.

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Cerruela García, G., García-Pedrajas, N. Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates. J Comput Aided Mol Des 32, 1273–1294 (2018). https://doi.org/10.1007/s10822-018-0171-5

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