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Error curves for evaluating the quality of feature rankings

In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measur...

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Detalles Bibliográficos
Autores principales: Slavkov, Ivica, Petković, Matej, Geurts, Pierre, Kocev, Dragi, Džeroski, Sašo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924685/
https://www.ncbi.nlm.nih.gov/pubmed/33816961
http://dx.doi.org/10.7717/peerj-cs.310
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author Slavkov, Ivica
Petković, Matej
Geurts, Pierre
Kocev, Dragi
Džeroski, Sašo
author_facet Slavkov, Ivica
Petković, Matej
Geurts, Pierre
Kocev, Dragi
Džeroski, Sašo
author_sort Slavkov, Ivica
collection PubMed
description In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measures of two chains of predictive models. The models in the first chain are built on nested sets of top-ranked features, while the models in the other chain are built on nested sets of bottom ranked features. We investigate which predictive models are appropriate for building these chains, showing empirically that the proposed method gives meaningful results and can detect differences in feature ranking quality. This is first demonstrated on synthetic data, and then on several real-world classification benchmark problems.
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spelling pubmed-79246852021-04-02 Error curves for evaluating the quality of feature rankings Slavkov, Ivica Petković, Matej Geurts, Pierre Kocev, Dragi Džeroski, Sašo PeerJ Comput Sci Algorithms and Analysis of Algorithms In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measures of two chains of predictive models. The models in the first chain are built on nested sets of top-ranked features, while the models in the other chain are built on nested sets of bottom ranked features. We investigate which predictive models are appropriate for building these chains, showing empirically that the proposed method gives meaningful results and can detect differences in feature ranking quality. This is first demonstrated on synthetic data, and then on several real-world classification benchmark problems. PeerJ Inc. 2020-12-07 /pmc/articles/PMC7924685/ /pubmed/33816961 http://dx.doi.org/10.7717/peerj-cs.310 Text en © 2020 Slavkov et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Slavkov, Ivica
Petković, Matej
Geurts, Pierre
Kocev, Dragi
Džeroski, Sašo
Error curves for evaluating the quality of feature rankings
title Error curves for evaluating the quality of feature rankings
title_full Error curves for evaluating the quality of feature rankings
title_fullStr Error curves for evaluating the quality of feature rankings
title_full_unstemmed Error curves for evaluating the quality of feature rankings
title_short Error curves for evaluating the quality of feature rankings
title_sort error curves for evaluating the quality of feature rankings
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924685/
https://www.ncbi.nlm.nih.gov/pubmed/33816961
http://dx.doi.org/10.7717/peerj-cs.310
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