Machine Learning Metrics for Network Datasets Evaluation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00375340" target="_blank" >RIV/68407700:21240/24:00375340 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-56326-3_22" target="_blank" >https://doi.org/10.1007/978-3-031-56326-3_22</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-56326-3_22" target="_blank" >10.1007/978-3-031-56326-3_22</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Metrics for Network Datasets Evaluation
Original language description
High-quality datasets are an essential requirement for leveraging machine learning (ML) in data processing and recently in network security as well. However, the quality of datasets is overlooked or underestimated very often. Having reliable metrics to measure and describe the input dataset enables the feasibility assessment of a dataset. Imperfect datasets may require optimization or updating, e.g., by including more data and merging class labels. Applying ML algorithms will not bring practical value if a dataset does not contain enough information. This work addresses the neglected topics of dataset evaluation and missing metrics. We propose three novel metrics to estimate the quality of an input dataset and help with its improvement or building a new dataset. This paper describes experiments performed on public datasets to show the benefits of the proposed metrics and theoretical definitions for more straightforward interpretation. Additionally, we have implemented and published Python code so that the metrics can be adopted by the worldwide scientific community.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/VJ02010024" target="_blank" >VJ02010024: Flow-based Encrypted Traffic Analysis</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
ICT Systems Security and Privacy Protection
ISBN
978-3-031-56326-3
ISSN
1868-4238
e-ISSN
1868-422X
Number of pages
14
Pages from-to
307-320
Publisher name
Springer
Place of publication
Cham
Event location
Poznan
Event date
Jun 14, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001294776100022