print

Interpretable machine learning for SME financial distress prediction

author
Medianovskyi, Kyrylo
Malakauskas, Aidas
Lakstutiene, Ausrine
Ben Yahia, Sadok
statement of authorship
Kyrylo Medianovskyi, Aidas Malakauskas, Ausrine Lakstutiene and Sadok Ben Yahia
source
12th International Conference on Information Systems and Advanced Technologies "ICISAT 2022" : Intelligent Information, Data Science and Decision Support System
location of publication
Cham
publisher
Springer
year of publication
2023
pages
p. 454 - 464
series
Lecture notes in networks and systems ; 624
conference name, date
12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022, 26-27 August 2022
conference location
Virtual, Online
url
https://doi.org/10.1007/978-3-031-25344-7_42
subject term
tehisõpe
finantskriisid
tehisintellekt
väikeettevõtted
keskmise suurusega ettevõtted
Scopus
Article collection metrics at Scopus
Article at Scopus
kvartiil
Q4
category (general)
Computer science
Engineering
category (sub)
Computer science. Signal processing
Engineering. Control and systems engineering
Computer science. Computer networks and communications
keyword
explainable AI
financial distress prediction
SHAP
SME
ISSN
2367-3370
ISBN
9783031253430
notes
Bibliogr.: 27 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
tarkvarateaduse instituut
language
inglise
Medianovskyi, K., Malakauskas, A., Lakstutiene, A., Yahia, S.B. Interpretable machine learning for SME financial distress prediction // 12th International Conference on Information Systems and Advanced Technologies "ICISAT 2022" : Intelligent Information, Data Science and Decision Support System. Cham : Springer, 2023. p. 454 - 464. (Lecture notes in networks and systems ; 624). https://doi.org/10.1007/978-3-031-25344-7_42