0561617 - ÚI 2024 RIV GB eng J - Journal Article
Sheikhi, A. - Mesiar, R. - Holeňa, Martin
A dimension reduction in neural network using copula matrix.
International Journal of General Systems. Roč. 52, č. 2 (2023), s. 131-146. ISSN 0308-1079. E-ISSN 1563-5104
Institutional support: RVO:67985807
Keywords : Principal component * copula * neural network * correlation * association measure
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.4, year: 2023
Method of publishing: Limited access
https://dx.doi.org/10.1080/03081079.2022.2108029

In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.
Permanent Link: https://hdl.handle.net/11104/0334185