bibtype C - Conference Paper (international conference)
ARLID 0640443
utime 20251107113508.5
mtime 20251027235959.9
DOI 10.5220/0013705300003982
title (primary) (eng) Categorical Model Estimation with Feature Selection Using an Ant Colony Optimization
specification
page_count 8 s.
media_type E
serial
ARLID cav_un_epca*0640442
ISBN 978-989-758-770-2
ISSN 2184-2809
title Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - ICINCO 2025
page_num 219-226
publisher
place Setubal
name SciTePress
year 2025
editor
name1 Gini
name2 Giuseppina
editor
name1 Precup
name2 Radu-Emil
editor
name1 Filev
name2 Dimitar
keyword categorical model estimation
keyword feature selection
keyword ant colony optimization
keyword dimension reduction
author (primary)
ARLID cav_un_auth*0452994
name1 Reznychenko
name2 T.
country CZ
author
ARLID cav_un_auth*0383037
name1 Uglickich
name2 Evženie
institution UTIA-B
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
full_dept Department of Signal Processing
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101167
name1 Nagy
name2 Ivan
institution UTIA-B
full_dept (cz) Zpracování signálů
full_dept Department of Signal Processing
department (cz) ZS
department ZS
full_dept Department of Signal Processing
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
source_type pdf
url https://library.utia.cas.cz/separaty/2025/ZS/uglickich-0640443.pdf
cas_special
project
project_id 9A22004
agency GA MŠk
country CZ
ARLID cav_un_auth*0459135
abstract (eng) 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.
action
ARLID cav_un_auth*0495799
name ICINCO 2025 : International Conference on Informatics in Control, Automation and Robotics /22./
dates 20251020
mrcbC20-s 20251022
place Marbella
country ES
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2026
num_of_auth 3
presentation_type PO
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0371244
confidential S
mrcbT16-q 5
mrcbT16-y 20.06
mrcbT16-x 0.45
mrcbT16-3 40
arlyear 2025
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 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
mrcbU67 Gini Giuseppina 340
mrcbU67 Precup Radu-Emil 340
mrcbU67 Filev Dimitar 340