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Feature selection with the R package MXM
Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly av...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792475/ https://www.ncbi.nlm.nih.gov/pubmed/31656581 http://dx.doi.org/10.12688/f1000research.16216.2 |
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author | Tsagris, Michail Tsamardinos, Ioannis |
author_facet | Tsagris, Michail Tsamardinos, Ioannis |
author_sort | Tsagris, Michail |
collection | PubMed |
description | Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications. |
format | Online Article Text |
id | pubmed-6792475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-67924752019-10-25 Feature selection with the R package MXM Tsagris, Michail Tsamardinos, Ioannis F1000Res Software Tool Article Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications. F1000 Research Limited 2019-09-30 /pmc/articles/PMC6792475/ /pubmed/31656581 http://dx.doi.org/10.12688/f1000research.16216.2 Text en Copyright: © 2019 Tsagris M and Tsamardinos I http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Tsagris, Michail Tsamardinos, Ioannis Feature selection with the R package MXM |
title | Feature selection with the R package
MXM
|
title_full | Feature selection with the R package
MXM
|
title_fullStr | Feature selection with the R package
MXM
|
title_full_unstemmed | Feature selection with the R package
MXM
|
title_short | Feature selection with the R package
MXM
|
title_sort | feature selection with the r package
mxm |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792475/ https://www.ncbi.nlm.nih.gov/pubmed/31656581 http://dx.doi.org/10.12688/f1000research.16216.2 |
work_keys_str_mv | AT tsagrismichail featureselectionwiththerpackagemxm AT tsamardinosioannis featureselectionwiththerpackagemxm |