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

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.

journal_name

J Comput Aided Mol Des

authors

Cerruela García G,García-Pedrajas N

doi

10.1007/s10822-018-0171-5

subject

Has Abstract

pub_date

2018-11-01 00:00:00

pages

1273-1294

issue

11

eissn

0920-654X

issn

1573-4951

pii

10.1007/s10822-018-0171-5

journal_volume

32

pub_type

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