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Are screening methods useful in feature selection?

Title: Are screening methods useful in feature selection?: An empirical study.
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Name(s): Wang, Mingyuan, author
Barbu, Adrian, author
Type of Resource: text
Genre: Text
Journal Article
Date Issued: 2019-09-11
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.
Identifier: FSU_libsubv1_scholarship_submission_1568294804_edd95dc1 (IID), 10.1371/journal.pone.0220842 (DOI)
Keywords: filter methods, screening methods, feature selection, machine learning, high dimensional data
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1568294804_edd95dc1
Use and Reproduction: Creative Commons Attribution (CC BY 4.0)
Host Institution: FSU
Is Part Of: PLoS One.
Issue: iss. 9, vol. 14

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Wang, M., & Barbu, A. (2019). Are screening methods useful in feature selection?: An empirical study. Plos One. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1568294804_edd95dc1

Title: Are screening methods useful in feature selection?: An empirical study.
Name(s): Wang, Mingyuan, author
Barbu, Adrian, author
Type of Resource: text
Genre: Text
Journal Article
Date Issued: 2019-09-11
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.
Identifier: FSU_libsubv1_scholarship_submission_1568294804_edd95dc1_Comp (IID), 10.1371/journal.pone.0220842 (DOI)
Keywords: filter methods, screening methods, feature selection, machine learning, high dimensional data
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1568294804_edd95dc1_Comp
Use and Reproduction: Creative Commons Attribution (CC BY 4.0)
Host Institution: FSU
Is Part Of: PLoS One.
Issue: iss. 9, vol. 14