Addressing the Cold Start Problem in Active Learning Approach Used For Semi-automated Sleep Stages Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00328046" target="_blank" >RIV/68407700:21230/18:00328046 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/18:00328046

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8621434" target="_blank" >https://ieeexplore.ieee.org/document/8621434</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM.2018.8621434" target="_blank" >10.1109/BIBM.2018.8621434</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Addressing the Cold Start Problem in Active Learning Approach Used For Semi-automated Sleep Stages Classification

Popis výsledku v původním jazyce

Classification of a PSG record to individual sleep stages is an expensive and time-consuming process because a trained human annotator (typically a physician) has to go through all segments of the record and classify them to their classes. In consequence, many semi-automated methods have been proposed in order to reduce the expert’s effort. The active learning approach is also well-suited for this type of task because it allows to select only the most informative instances for labeling without the quality of classification to be reduced. On the other hand the unsatisfactory initialization of active learning can cause a slower learning process. In this paper we introduce the method for creating of the initial set of labeled instances to eliminate this threat. Because k-means algorithm is commonly used as the initialization method, the comparison between these two methods is provided.

Název v anglickém jazyce

Addressing the Cold Start Problem in Active Learning Approach Used For Semi-automated Sleep Stages Classification

Popis výsledku anglicky

Classification of a PSG record to individual sleep stages is an expensive and time-consuming process because a trained human annotator (typically a physician) has to go through all segments of the record and classify them to their classes. In consequence, many semi-automated methods have been proposed in order to reduce the expert’s effort. The active learning approach is also well-suited for this type of task because it allows to select only the most informative instances for labeling without the quality of classification to be reduced. On the other hand the unsatisfactory initialization of active learning can cause a slower learning process. In this paper we introduce the method for creating of the initial set of labeled instances to eliminate this threat. Because k-means algorithm is commonly used as the initialization method, the comparison between these two methods is provided.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA17-20480S" target="_blank" >GA17-20480S: Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signálu</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Proceedings

ISBN

978-1-5386-5488-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

2249-2253

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Madrid

Datum konání akce

3. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458654000382