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Dipolar Data Integration Through Univariate, Binary Classifiers

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Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

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Abstract

Aggregation of large data sets is one of the current topics of exploratory analysis and pattern recognition. Integration of data sets is a useful and necessary step towards knowledge extraction from large data sets. The possibility of separable integration of multidimensional data sets by one dimensional binary classifiers is analyzed in the paper, as well as designing a layer of binary classifiers for separable aggregation. The optimization problem of separable layer designing is formulated. A dipolar strategy aimed at optimizing separable aggregation of large data sets is proposed in the presented paper.

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Acknowledgments

The presented study was supported by the grant S/WI/2/2013 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Leon Bobrowski

Authors
  1. Leon Bobrowski
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Correspondence to Leon Bobrowski .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Bobrowski, L. (2017). Dipolar Data Integration Through Univariate, Binary Classifiers. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-67074-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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