@inproceedings{bfd57d8e7a344d49a2a512e3b3324c7c,
title = "Dynamic classifier and sensor using small memory buffers",
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.",
keywords = "Big data, Classification, Clustering, Dynamic classifier, Dynamic rules, Memory buffers, Sensing data",
author = "R. Gelbard and A. Khalemsky",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 18th Industrial Conference on Data Mining, ICDM 2018 ; Conference date: 11-07-2018 Through 12-07-2018",
year = "2018",
doi = "https://doi.org/10.1007/978-3-319-95786-9_13",
language = "الإنجليزيّة",
isbn = "9783319957852",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "173--182",
editor = "Petra Perner",
booktitle = "Advances in Data Mining. Applications and Theoretical Aspects - 18th Industrial Conference, ICDM 2018, Proceedings",
address = "ألمانيا",
}