0640443 20260226074613.220251027235959.9 105022087375 10.5220/0013705300003982 8 s. E cav_un_epca*0640442978-989-758-770-22184-2809219-226SetubalSciTePress2025GiniGiuseppinaPrecupRadu-EmilFilevDimitar categorical model estimation feature selection ant colony optimization dimension reduction cav_un_auth*0452994 Reznychenko T. CZ cav_un_auth*0383037 Uglickich Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf https://library.utia.cas.cz/separaty/2025/ZS/uglickich-0640443.pdf 9A22004 GA MŠk CZ cav_un_auth*0459135 This paper deals with the analysis of high-dimensional discrete data values from questionnaires, with the aim of identifying explanatory variables that influence a target variable. We propose a hybrid algorithm that combines categorical model estimation with an ant colony optimization scheme for feature selection. The main contributions are: (i) the efficient selection of the most significant explanatory variables, and (ii) the estimation of a categorical model with reduced dimensionality. Experimental results and comparisons with well-known algorithms (e.g., random forest, categorical boosting, k-nearest neighbors) and feature selection techniques are presented. cav_un_auth*0495799 ICINCO 2025 : International Conference on Informatics in Control, Automation and Robotics /22./ 20251020 20251022 Marbella ES BB 10000 10100 10103 2026 3 PO RVO:67985556 https://hdl.handle.net/11104/0371244 S 5 20.06 0.45 40 2025 105022087375 SCOPUS PUBMED WOS cav_un_epca*0640442 Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - ICINCO 2025 SciTePress 2025 Setubal 219 226 978-989-758-770-2 2184-2809 Gini Giuseppina 340 Precup Radu-Emil 340 Filev Dimitar 340