<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on <italic>n</italic> = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model’s accuracy compared to the second-best meta-learning method.</p>
</abstract>

There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on n = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model’s accuracy compared to the second-best meta-learning method.

FSPL: A Meta–Learning Approach for a Filter and Embedded Feature Selection Pipeline

University College London Cancer Institute

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS...

FSPL: A Meta–Learning Approach for a Filter and Embedded Feature Selection Pipeline

Online veröffentlicht: 29. März 2023

Seitenbereich: 103 - 115

Eingereicht: 17. Feb. 2022

Akzeptiert: 27. Sept. 2022

DOI: https://doi.org/10.34768/amcs-2023-0009

Schlüsselwörter
feature selection pipeline, meta-learning, no free lunch, autoML, genetic algorithm

© 2023 Teddy Lazebnik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

FSPL: A Meta–Learning Approach for a Filter and Embedded Feature Selection Pipeline

Online veröffentlicht: 29. März 2023

Seitenbereich: 103 - 115

Eingereicht: 17. Feb. 2022

Akzeptiert: 27. Sept. 2022

DOI: https://doi.org/10.34768/amcs-2023-0009

Schlüsselwörterfeature selection pipeline, meta-learning, no free lunch, autoML, genetic algorithm

© 2023 Teddy Lazebnik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
feature selection pipeline, meta-learning, no free lunch, autoML, genetic algorithm