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Stacking-Based Integrated Machine Learning with Data Reduction

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

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Abstract

Integrated machine learning is understood as integration of the data reduction with the learning process. Such integration allows to introduce adaptation mechanisms within the learning process by modification of the data with a view to finding its better representation from the point of view of the learning performance criterion. Data modification can be carried out through data reduction in both dimensions, i.e. the feature and the instance ones producing the set of prototypes. Currently, data reduction has become a crucial technique for big data analysis and improvement of the machine learning process results. In this paper the stacking technique has been proposed for improving the process of the integrated machine classification and to assure diversification among prototypes. To validate the proposed approach we have carried-out computational experiment. The paper includes the description of the approach and the discussion of the validating experiment results.

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Ireneusz Czarnowski & Piotr Jędrzejowicz

Authors
  1. Ireneusz Czarnowski
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  2. Piotr Jędrzejowicz
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Corresponding author

Correspondence to Ireneusz Czarnowski .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Czarnowski, I., Jędrzejowicz, P. (2018). Stacking-Based Integrated Machine Learning with Data Reduction. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-59421-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59420-0

  • Online ISBN: 978-3-319-59421-7

  • eBook Packages: EngineeringEngineering (R0)

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