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Rescaling for Evaluations Using Inclusion-Exclusion Integral

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 442))

Abstract

On multivariate analyses generally distributions of explanatory variable have deviation depending on each unique quality, and eliminating deviation often beneficially effective. We propose two algorithms for rescaling of raw data and verify the validity of them using real reliable big data.

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Honda, A., Fukuda, R., Okamoto, J. (2014). Rescaling for Evaluations Using Inclusion-Exclusion Integral. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 442. Springer, Cham. https://doi.org/10.1007/978-3-319-08795-5_30

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  • DOI: https://doi.org/10.1007/978-3-319-08795-5_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08794-8

  • Online ISBN: 978-3-319-08795-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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