Skip to main content
SpringerLink
Log in

Software Tools

  • Chapter
  • First Online:
Recent Advances in Ensembles for Feature Selection

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 147))

Abstract

This chapter provides the users with a review of some popular software tools that can help in the design of their ensembles for feature selection. There is an important number of feature selection and ensemble learning methods already implemented and available in different platforms, so it is useful to know them before coding our own ensembles. Section 9.1 comments on the methods available in different popular software tools, such as Matlab, Weka, R, scikit-learn, or more recent and sophisticated platforms for parallel learning. Then, Sect. 9.2 gives some examples of code in Matlab.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://CRAN.R-project.org/package=caret.

  2. 2.

    https://CRAN.R-project.org/package=Boruta.

  3. 3.

    https://CRAN.R-project.org/package=adabag.

  4. 4.

    https://CRAN.R-project.org/package=randomForest.

  5. 5.

    https://CRAN.R-project.org/package=gbm.

  6. 6.

    https://CRAN.R-project.org/package=EFS.

  7. 7.

    http://efs.heiderlab.de.

  8. 8.

    https://CRAN.R-project.org/package=mRMRe.

  9. 9.

    https://sourceforge.net/projects/rm-featselext/.

  10. 10.

    http://featureselection.asu.edu/index.php.

  11. 11.

    https://github.com/sramirez/spark-infotheoretic-feature-selection.

  12. 12.

    http://www.lidiagroup.org/index.php/en/materials-en.html.

  13. 13.

    https://github.com/sramirez/flink-infotheoretic-feature-selection.

  14. 14.

    https://github.com/sramirez/fast-mRMR.

  15. 15.

    http://lidiagroup.org/index.php/en/materials-en.html.

References

  1. MATLAB. version 8.1.0.604 (R2013a). The MathWorks Inc. (2013)

    Google Scholar 

  2. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The Weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  3. Neumann, U., Genze, N., Heider, D.: EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData Min. 10(1), 21 (2017)

    Article  Google Scholar 

  4. Alcalá-Fernández, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multi.-Valued Log. Soft Comput. 17(2–3), 255–287 (2011)

    Google Scholar 

  5. Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. CRC Press, Boca Raton (2013)

    Google Scholar 

  6. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  7. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  8. Apache Hadoop. http://hadoop.apache.org/

  9. Apache Spark. https://spark.apache.org

  10. MLib/Apache Spark. https://spark.apache.org/mllib

  11. Ramirez-Gallego, S., Mouriño-Talin, S., Martinez-Rego, D., Bolon-Canedo, V., Benitez, J.M., Alonso-Betanzos, A., Herrera, F.: An information theory-based feature selection framework for big data under Apache Spark. IEEE Trans. Syst. Man Cybern. Syst. (2018). (in press)

    Google Scholar 

  12. Apache Flink. https://flink.apache.org/

  13. Ramirez-Gallego, S., Lastra, I., Martinez-Rego, D., Bolón-Canedo, V., Benitez, J.M., Alonso-Betanzos, A., Herrera, F.: Fast-mRMR: fast minimum redundancy maximum relevance algorithm for high-dimensional big data. Int. J. Intell. Syst. 32(2), 134–152 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Informática, Universidade da Coruña, A Coruña, Spain

    Verónica Bolón-Canedo & Amparo Alonso-Betanzos

Authors
  1. Verónica Bolón-Canedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amparo Alonso-Betanzos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Verónica Bolón-Canedo .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bolón-Canedo, V., Alonso-Betanzos, A. (2018). Software Tools. In: Recent Advances in Ensembles for Feature Selection. Intelligent Systems Reference Library, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-90080-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90080-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90079-7

  • Online ISBN: 978-3-319-90080-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation