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Dynamic Classifier and Sensor Using Small Memory Buffers

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10933))

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Abstract

A model presented in current paper designed for dynamic classifying of real time cases received in a stream of big sensing data. The model comprises multiple remote autonomous sensing systems; each generates a classification scheme comprising a plurality of parameters. The classification engine of each sensing system is based on small data buffers, which include a limited set of “representative” cases for each class (case-buffers). Upon receiving a new case, the sensing system determines whether it may be classified into an existing class or it should evoke a change in the classification scheme. Based on a threshold of segmentation error parameter, one or more case-buffers are dynamically regrouped into a new composition of buffers, according to a criterion of segmentation quality.

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Acknowledgment

This work was supported in part by a grant from the MAGNET program of the Israeli Innovation Authority; who also submitted this work as a patent application.

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Authors and Affiliations

  1. Information Systems, Bar-Ilan University, Ramat-Gan, 5290002, Israel

    R. Gelbard & A. Khalemsky

Authors
  1. R. Gelbard
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  2. A. Khalemsky
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Corresponding authors

Correspondence to R. Gelbard or A. Khalemsky .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Gelbard, R., Khalemsky, A. (2018). Dynamic Classifier and Sensor Using Small Memory Buffers. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-95786-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95785-2

  • Online ISBN: 978-3-319-95786-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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