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Distributed Monte Carlo Feature Selection: Extracting Informative Features Out of Multidimensional Problems with Linear Speedup

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

Selection of informative features out of ever growing results of high throughput biological experiments requires specialized feature selection algorithms. One of such methods is the Monte Carlo Feature Selection - a straightforward, yet computationally expensive one. In this technical paper we present architecture and performance of a development version of our distributed implementation of this algorithm, designed to run in multiprocessor as well as multihost computing environments, and potentially controllable through a web browser by non-IT staff. As a simple enhancement, our method is able to produce statistically interpretable output by means of permutation testing. Tested on reference Golub et al. leukemia data, as well as on our own dataset of almost 2 million features, it has shown nearly linear speedup when executed with an increased amount of processors. Being platform independent, as well as open for extensions, this application could become a valuable tool for researchers facing the challenge of ill-defined high dimensional feature selection problems.

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Acknowledgements

I would like to thank dr Draminski for providing the latest version of dmLab software for evaluation, as well as Najla Al-Harbi, Sara Bin Judia, dr Salma Majid, dr Ghazi Alsbeih (Faisal Specialist Hospital & Research Centre, Riyadh 11211, Kingdom of Saudi Arabia), and furthermore Bozena Rolnik (Data Mining Group) for providing the CNV data. Calculations were carried out using the computer cluster Ziemowit (http://www.ziemowit.hpc.polsl.pl) funded by the Silesian BIO-FARMA project No. POIG.02.01.00-00-166/08 in the Computational Biology and Bioinformatics Laboratory of the Biotechnology Centre in the Silesian University of Technology. The work was financially supported by NCN grant HARMONIA UMO-2013/08/M/ST6/00924 (LK).

Finally I would like to thank Anonymous Reviewers who helped to increase quality of the paper.

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Authors and Affiliations

  1. Faculty of Automatic Control, Electronics and Computer Science, Data Mining Group, Silesian University of Technology, Gliwice, Poland

    Lukasz Krol

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  1. Lukasz Krol
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Correspondence to Lukasz Krol .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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© 2016 Springer International Publishing Switzerland

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Krol, L. (2016). Distributed Monte Carlo Feature Selection: Extracting Informative Features Out of Multidimensional Problems with Linear Speedup. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_35

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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