Fuzzy Masks for Correlation Matrix Pruning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63585098" target="_blank" >RIV/70883521:28140/25:63585098 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10896637" target="_blank" >https://ieeexplore.ieee.org/document/10896637</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3544027" target="_blank" >10.1109/ACCESS.2025.3544027</a>
Alternative languages
Result language
angličtina
Original language name
Fuzzy Masks for Correlation Matrix Pruning
Original language description
Among statistical methods used in data analysis processes, correlation analysis holds one of the most significant places. Using correlation, analysts measure prediction potential between values of a pair of attributes of a dataset which can be summarized into a correlation matrix for any multidimensional dataset. Such matrices are usually sizable, and hard to read. Pruning of the correlation matrix for the precise selection of attribute pairs of a considered dataset which bear strong prediction potential is conventionally conducted via correlation matrix masks. Since these masks are commonly designed as crisp borders for the acceptability of values in the matrix, there is a strong lack of nuance in this approach. The work presented in the scope of this study focuses on the design and implementation of fuzzy masks for correlation matrices. This objective is divided into three main tasks - firstly, the visualization of correlation coefficient value frequency and its relationship to fuzzy membership function is designed and implemented, then visualisation of basic regression analysis and correlation context of attribute pairs in the fuzzy area of a studied dataset is designed and implemented, and lastly, the proposed approach is evaluated via case studies on three benchmarking datasets. The results obtained with the fuzzy approach to correlation matrix masks show a qualitative improvement in the matrix pruning task and a more appropriate identification of the relevant parts of the dataset compared to the conventional, crips approach.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
13
Issue of the periodical within the volume
Neuvedeno
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
35387-35400
UT code for WoS article
001433330600009
EID of the result in the Scopus database
2-s2.0-85218771741