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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/19:00335049

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019

ISBN

—

ISSN

2504-3900

e-ISSN

2504-3900

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

Multidisciplinary Digital Publishing Institute (MDPI AG)

Místo vydání

Basel

Místo konání akce

Toledo

Datum konání akce

2. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—