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Article

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Title

Framework for multi-criteria assessment of classification models for the purposes of credit scoring

Authors

[ 1 ] Wydział Techniczny, Akademia im. Jakuba z Paradyża | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology
[2.9] Mechanical engineering

Year of publication

2023

Published in

Journal of Big Data

Journal year: 2023 | Journal volume: 10 | Journal number: 94

Article type

scientific article

Publication language

english

Keywords
EN
  • Classification algorithms
  • Model evaluation
  • Multi‑criteria decision making
  • PROSA
  • PROMETHEE II
  • Credit scoring
Abstract

EN The main dilemma in the case of classification tasks is to find—from among many combinations of methods, techniques and values of their parameters—such a structure of the classifier model that could achieve the best accuracy and efficiency. The aim of the article is to develop and practically verify a framework for multi‑criteria evaluation of classification models for the purposes of credit scoring. The framework is based on the Multi‑Criteria Decision Making (MCDM) method called PROSA (PROMETHEE for Sustainability Analysis), which brought added value to the modelling process, allowing the assessment of classifiers to include the consistency of the results obtained on the training set and the validation set, and the consistency of the classification results obtained for the data acquired in different time periods. The study considered two aggregation scenarios of TSC (Time periods, Sub‑criteria, Criteria) and SCT (Sub‑criteria, Criteria, Time periods), in which very similar results were obtained for the evaluation of classification models. The leading positions in the ranking were taken by borrower classification models using logistic regression and a small number of predictive variables. The obtained rankings were compared to the assessments of the expert team, which turned out to be very similar.

Date of online publication

2023

Pages (from - to)

1 - 45

DOI

10.1186/s40537-023-00768-7

URL

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00768-7

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date

06.2023

Date of Open Access to the publication

at the time of publication

Ministry points / journal

140.0