Machine Learning Metrics for Network Datasets Evaluation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133655" target="_blank" >RIV/63839172:_____/24:10133655 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-56326-3_22" target="_blank" >https://link.springer.com/chapter/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>

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

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Article name in the collection

ICT Systems Security and Privacy Protection

ISBN

978-3-031-56325-6

ISSN

1868-4238

e-ISSN

—

Number of pages

13

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