Dynamic classifier and sensor using small memory buffers

R. Gelbard; A. Khalemsky

doi:10.1007/978-3-319-95786-9_13

Dynamic classifier and sensor using small memory buffers

R. Gelbard, A. Khalemsky

School of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings
Editors	Petra Perner
Publisher	Springer Verlag
Pages	173-182
Number of pages	10
ISBN (Print)	9783319957852
DOIs	https://doi.org/10.1007/978-3-319-95786-9_13
State	Published - 2018
Event	18th Industrial Conference on Data Mining, ICDM 2018 - New York, United States Duration: 11 Jul 2018 → 12 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10933 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Industrial Conference on Data Mining, ICDM 2018
Country/Territory	United States
City	New York
Period	11/07/18 → 12/07/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

Keywords

Big data
Classification
Clustering
Dynamic classifier
Dynamic rules
Memory buffers
Sensing data

Access to Document

10.1007/978-3-319-95786-9_13

Cite this

Gelbard, R., & Khalemsky, A. (2018). Dynamic classifier and sensor using small memory buffers. In P. Perner (Ed.), Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings (pp. 173-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10933 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-95786-9_13

Gelbard, R. ; Khalemsky, A. / Dynamic classifier and sensor using small memory buffers. Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings. editor / Petra Perner. Springer Verlag, 2018. pp. 173-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Gelbard, R & Khalemsky, A 2018, Dynamic classifier and sensor using small memory buffers. in P Perner (ed.), Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10933 LNAI, Springer Verlag, pp. 173-182, 18th Industrial Conference on Data Mining, ICDM 2018, New York, United States, 11/07/18. https://doi.org/10.1007/978-3-319-95786-9_13

Dynamic classifier and sensor using small memory buffers. / Gelbard, R.; Khalemsky, A.
Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings. ed. / Petra Perner. Springer Verlag, 2018. p. 173-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10933 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Gelbard R, Khalemsky A. Dynamic classifier and sensor using small memory buffers. In Perner P, editor, Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings. Springer Verlag. 2018. p. 173-182. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-95786-9_13

Dynamic classifier and sensor using small memory buffers

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