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Collinear Data Structures and Interaction Models

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

Models of interactions between multiple fetaures (genes) can be obtained from collinear data sets consisting of multivariate feature vectors. Such models can be designed by minimizing the collinearity criterion function with the basis echange algorithm.

The collinearity criterion functions are defined on learning data subsets representing selected categories (e.g. diseases). Based on the minimization of the collinearity functions, interaction models specific to each category can be defined.

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Acknowledgments

The presented study was supported by the grant WZ/WI-IIT/3/2020 from the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok, Poland

    Leon Bobrowski

  2. Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

    Leon Bobrowski

Authors
  1. Leon Bobrowski
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Correspondence to Leon Bobrowski .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Bobrowski, L. (2022). Collinear Data Structures and Interaction Models. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_30

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16013-4

  • Online ISBN: 978-3-031-16014-1

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