PerQoDA: strength of association between data and labels as a measure of dataset quality in network traffic classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F25%3A10133786" target="_blank" >RIV/63839172:_____/25:10133786 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/25:00387724
Result on the web
<a href="https://doi.org/10.1007/s10489-025-06925-0" target="_blank" >https://doi.org/10.1007/s10489-025-06925-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-025-06925-0" target="_blank" >10.1007/s10489-025-06925-0</a>

Result language
angličtina
Original language name
PerQoDA: strength of association between data and labels as a measure of dataset quality in network traffic classification
Original language description
Intelligent and autonomous networks require precise and fast mechanisms to minimize errors and ensure efficient operation. Modern methods are increasingly based on artificial intelligence, in particular on machine learning, to reliably process large amounts of data. While high-quality datasets are essential to train machine learning models, assessing the quality of datasets can be challenging and is often overlooked or underestimated. This paper proposes a novel method of permutation testing to assess one relevant dataset quality dimension in the context of binary or multiclass classification problems: the strength of the association between data and labels. The method described is called Permutation for Quality of Dataset Assessment (PerQoDA). In this paper we introduce the method, the statistics and visualisations necessary for proper interpretation of the results, and lastly, the theoretical justification of limits of performance. According to our experiments carried out on both simulated as well as real network datasets, the PerQoDA method can correctly estimate the strength of relationships in labelled datasets across a range of scenarios.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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)

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)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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