A framework for feature selection through boosting

Ahmad Alsahaf*, Nicolai Petkov, Vikram Shenoy, George Azzopardi

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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Samenvatting

As dimensions of datasets in predictive modelling continue to grow, feature selection becomes increasingly practical. Datasets with complex feature interactions and high levels of redundancy still present a challenge to existing feature selection methods. We propose a novel framework for feature selection that relies on boosting, or sample re-weighting, to select sets of informative features in classification problems. The method uses as its basis the feature rankings derived from fast and scalable tree-boosting models, such as XGBoost. We compare the proposed method to standard feature selection algorithms on 9 benchmark datasets. We show that the proposed approach reaches higher accuracies with fewer features on most of the tested datasets, and that the selected features have lower redundancy.
Originele taal-2English
Artikelnummer115895
TijdschriftExpert systems with applications
Volume187
Vroegere onlinedatum16-sep-2021
DOI's
StatusPublished - jan-2022

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