Algorithmic Feature Selection and Dimensionality Reduction in Signal Classification Tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F24%3A00603949" target="_blank" >RIV/61388998:_____/24:00603949 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-52965-8_15" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-52965-8_15</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-52965-8_15" target="_blank" >10.1007/978-3-031-52965-8_15</a>
Alternative languages
Result language
angličtina
Original language name
Algorithmic Feature Selection and Dimensionality Reduction in Signal Classification Tasks
Original language description
This paper presents a research endeavour addressing the recognition of acoustic emission signals, aiming to enhance their utilisation in non-destructive defectoscopy and machining process control. The classification task can be accomplished through two approaches: representing signals using a suitable attribute set, or directly passing the signals in their entirety to the classification algorithm. Our primary focus was on the meticulous selection of methods and tools for automating the extraction of a comprehensive set of features from the signals, followed by dimensionality reduction techniques. Subsequently, we conducted a comprehensive performance evaluation by comparing various classifiers applied to the low-dimensional projections. Lastly, we put the feature based classification approach to the test with direct signal classification employing convolutional neural networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Springer Proceedings in Mathematics and Statistics
ISBN
978-3-031-52964-1
ISSN
2194-1009
e-ISSN
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Number of pages
6
Pages from-to
187-193
Publisher name
Springer
Place of publication
CHAM
Event location
Bělehrad
Event date
Aug 23, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001263810800015