Comparison of Controlled Undersampling Methods for Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133704" target="_blank" >RIV/63839172:_____/24:10133704 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10467755" target="_blank" >https://ieeexplore.ieee.org/document/10467755</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACDSA59508.2024.10467755" target="_blank" >10.1109/ACDSA59508.2024.10467755</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of Controlled Undersampling Methods for Machine Learning
Original language description
Data reduction is an important preprocessing operation for Machine Learning to learn from large datasets, especially in the case of applications requiring online learning using constrained resources. Our survey focuses on a specific family of data reduction methods - controlled undersampling methods. We observe the behaviour of the methods as they cooperate with several supervised machine-learning techniques over multiple evaluation datasets. Our results show that the random undersampling method offers surprisingly good results compared to more complex methods and is a good fit for online and resource-sensitive machine-learning applications.
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
<a href="/en/project/LM2023054" target="_blank" >LM2023054: e-Infrastructure CZ</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
ISBN
979-8-3503-9452-8
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Neuveden
Event location
Victoria, Seychelles
Event date
Feb 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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