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Estimation of Probability Density Function, Differential Entropy and Other Relative Quantities for Data Streams with Concept Drift

  • Maciej JaworskiEmail author
  • Patryk Najgebauer
  • Piotr Goetzen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

In this paper estimators of nonstationary probability density function are proposed. Additionally, applying the trapezoidal method of numerical integration, the estimators of two information-theoretic measures are presented: the differential entropy and the Renyi’s quadratic differential entropy. Finally, using an analogous methodology, estimators of the Cauchy-Schwarz divergence and the probability density function divergence are proposed, which are used to measure the differences between two probability density functions. All estimators are proposed in two variants: one with the sliding window and one with the forgetting factor. Performance of all the estimators is verified using numerical simulations.

Keywords

Data stream Concept drift Density estimation Differential entropy Kernel function 

Notes

Acknowledgments

This work was supported by the Polish National Science Centre under Grant No. 2014/15/B/ST7/05264.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maciej Jaworski
    • 1
    Email author
  • Patryk Najgebauer
    • 1
  • Piotr Goetzen
    • 2
    • 3
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzestochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesŁódźPoland
  3. 3.Clark UniversityWorcesterUSA

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