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Imprecise Statistical Inference for Accelerated Life Testing Data: Imprecision Related to Log-Rank Test Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Density Estimation with Imprecise Kernels: Application to Classification

Authors : Guillaume Dendievel, Sebastien Destercke, Pierre Wachalski

Published in: Uncertainty Modelling in Data Science

Publisher: Springer International Publishing

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Abstract

In this paper, we explore the problem of estimating lower and upper densities from imprecisely defined families of parametric kernels. Such estimations allow to rely on a single bandwidth value, and we show that it provides good results on classification tasks when extending the naive Bayesian classifier.

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previous chapter Cycle-Free Cuts of the Reciprocal Relation Generated by Random Variables that are Pairwisely Coupled by a Frank Copula
next chapter Z-numbers as Generalized Probability Boxes
Footnotes
1
A kernel is here a symmetric, non-negative function with \(\int _{\mathbb {R}} K(y)dy=1\) and mean 0.
 
2
In some sense, to regularize our model.
 
Literature
1.
Corani, G., Zaffalon, M.: Learning reliable classifiers from small or incomplete data sets: the naive credal classifier 2. J. Mach. Learn. Res. 9, 581–621 (2008)MathSciNetMATH
2.
Destercke, S., Strauss, O.: Filtering with clouds. Soft Comput. 16(5), 821–831 (2012)CrossRef
3.
Silverman, B.W.: Density estimation for statistics and data analysis, vol. 26. CRC Press, Boca Raton (1986)CrossRef
4.
Wand, M.P., Jones, M.C.: Kernel smoothing. CRC Press, Boca Raton (1994)MATH
5.
6.
Zaffalon, M., Corani, G., Mauá, D.: Evaluating credal classifiers by utility-discounted predictive accuracy. Int. J. Approx. Reason. 53(8), 1282–1301 (2012)MathSciNetCrossRef
Metadata
Title
Density Estimation with Imprecise Kernels: Application to Classification
Authors
Guillaume Dendievel
Sebastien Destercke
Pierre Wachalski
Copyright Year
2019
Book
Uncertainty Modelling in Data Science

Print ISBN: 978-3-319-97546-7

Electronic ISBN: 978-3-319-97547-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-97547-4_9

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