Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain.
J Comput Aided Mol Des. 2018 Nov;32(11):1273-1294. doi: 10.1007/s10822-018-0171-5. Epub 2018 Oct 26.
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.
特征选择通常被用作机器学习的预处理步骤，以提高学习性能、降低计算复杂度和促进模型解释。本文提出将提升特征选择应用于改善标准特征选择算法的分类性能，这些算法用于预测 P-糖蛋白抑制剂和底物。使用两种著名的分类算法，决策树和支持向量机，对化合物进行分类。实验结果表明，在保持特征降维能力的同时，提升特征选择相对于标准特征选择算法具有更好的性能。
