Query-By-Committee Framework Used for Semi-Automatic Sleep Stages Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335049" target="_blank" >RIV/68407700:21230/19:00335049 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/19:00335049
Result on the web
<a href="https://www.mdpi.com/2504-3900/31/1/80" target="_blank" >https://www.mdpi.com/2504-3900/31/1/80</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/proceedings2019031080" target="_blank" >10.3390/proceedings2019031080</a>

Result language
angličtina
Original language name
Query-By-Committee Framework Used for Semi-Automatic Sleep Stages Classification
Original language description
Active learning is very useful for classification problems where it is hard or time-consuming to acquire classes of data in order to create a subset for training a classifier. The classification of over-night polysomnography records to sleep stages is an example of such application because an expert has to annotate a large number of segments of a record. Active learning methods enable us to iteratively select only the most informative instances for the manual classification so the total expert’s effort is reduced. However, the process is able to be insufficiently initialised because of a large dimensionality of polysomnography (PSG) data, so the fast convergence of active learning is at risk. In order to prevent this threat, we have proposed a variant of the query-by-committee active learning scenario which take into account all features of data so it is not necessary to reduce a feature space, but the process is quickly initialised. The proposed method is compared to random sampling and margin uncertainty sampling which is another well-known active learning method. It was shown that, during crucial first iteration of the process, the provided variant of query-by-committee acquired the best results among other strategies in most cases.
Czech name
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Czech description
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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)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019
ISBN
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ISSN
2504-3900
e-ISSN
2504-3900
Number of pages
9
Pages from-to
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Publisher name
Multidisciplinary Digital Publishing Institute (MDPI AG)
Place of publication
Basel
Event location
Toledo
Event date
Dec 2, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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