Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F22%3A43906130" target="_blank" >RIV/60076658:12310/22:43906130 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-14343-4_38" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-14343-4_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-14343-4_38" target="_blank" >10.1007/978-3-031-14343-4_38</a>
Alternative languages
Result language
angličtina
Original language name
Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series
Original language description
The paper discusses the Learning-based information (L) and Learning Entropy (LE) in contrast to classical Shannon probabilistic Information (I) and probabilistic entropy (H). It is shown that L corresponds to the recently introduced Approximate Individual Sample-point Learning Entropy (AISLE). For data series, then, the LE should be defined as the mean value of L that is finally in proper accordance with Shannon's concept of entropy H. The distinction of L against I is explained by the real-time anomaly detection of individual time series data points (states). First, the principal distinction of the information concept of Ivs.L is demonstrated in respect to data governing law that L considers explicitly (while I does not). Second, it is shown that L has the potential to be applied on much shorter datasets than I because of the learning system being pre-trained and being able to generalize from a smaller dataset. Then, floating window trajectories of the covariance matrix norm, the trajectory of approximate variance fractal dimension, and especially the windowed Shannon Entropy trajectory are compared to LE on multichannel EEG featuring epileptic seizure. The results on real time series show that L, i.e., AISLE, can be a useful counterpart to Shannon entropy allowing us also for more detailed search of anomaly onsets (change points).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2022 WORKSHOPS
ISBN
978-3-031-14343-4
ISSN
1865-0929
e-ISSN
1865-0937
Number of pages
14
Pages from-to
402-415
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Vienna Univ Econ & Business
Event date
Aug 22, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000870761200038