2022
1.
Overschie, Jeroen; Alsahaf, Ahmad; Azzopardi, George
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms Journal Article
Links | BibTeX | Altmetric | Tags: feature ranking, feature selection
@article{Overschie2022,
title = {fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms},
author = {Jeroen Overschie and Ahmad Alsahaf and George Azzopardi},
doi = {https://joss.theoj.org/papers/10.21105/joss.04611},
year = {2022},
date = {2022-11-23},
urldate = {2022-11-23},
journal = {Journal of Open Source Software},
keywords = {feature ranking, feature selection},
pubstate = {published},
tppubtype = {article}
}
2.
Alsahaf, Ahmad; Petkov, Nicolai; Shenoy, Vikram; Azzopardi, George
A framework for feature selection through boosting Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: feature ranking, feature selection, predictive analysis
@article{Alsahaf2022c,
title = {A framework for feature selection through boosting},
author = {Ahmad Alsahaf and Nicolai Petkov and Vikram Shenoy and George Azzopardi},
doi = {https://doi.org/10.1016/j.eswa.2021.115895},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Expert Systems with Applications},
volume = {187},
pages = {115895},
publisher = {Pergamon},
abstract = {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.},
keywords = {feature ranking, feature selection, predictive analysis},
pubstate = {published},
tppubtype = {article}
}
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.