Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling
Accurate model selection is essential in predictive modelling across various domains, significantly impacting decision-making and resource allocation. Despite extensive research, the model selection process remains challenging. This work aims to integrate the Minimum Description Length principle wit...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10849532/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825199947261149184 |
|---|---|
| author | Petr Silhavy Katerina Hlavackova-Schindler Radek Silhavy |
| author_facet | Petr Silhavy Katerina Hlavackova-Schindler Radek Silhavy |
| author_sort | Petr Silhavy |
| collection | DOAJ |
| description | Accurate model selection is essential in predictive modelling across various domains, significantly impacting decision-making and resource allocation. Despite extensive research, the model selection process remains challenging. This work aims to integrate the Minimum Description Length principle with the Multi-Criteria Decision Analysis to enhance the selection of forecasting machine learning models. The proposed MDL-MCDA framework combines the MDL principle, which balances model complexity and data fit, with the MCDA, which incorporates multiple evaluation criteria to address conflicting error measurements. Four datasets from diverse domains, including software engineering (effort estimation), healthcare (glucose level prediction), finance (GDP prediction), and stock market prediction, were used to validate the framework. Various regression models and feed-forward neural networks were evaluated using criteria such as MAE, MAPE, RMSE, and Adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. We employed the Analytic Hierarchy Process (AHP) to determine the relative importance of these criteria. We conclude that the integration of MDL and MCDA significantly improved model selection across all datasets. The cubic polynomial regression model and the multi-layer perceptron models outperformed other models in terms of AHP score and MDL criterion. Specifically, the MDL-MCDA approach provided a more nuanced evaluation, ensuring the selected models effectively balanced complexity and predictive accuracy. |
| format | Article |
| id | doaj-art-4eeac472dfeb42a39a0f611f24f522bd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4eeac472dfeb42a39a0f611f24f522bd2025-02-08T00:00:14ZengIEEEIEEE Access2169-35362025-01-0113193881940710.1109/ACCESS.2025.353281510849532Minimum Description Length and Multi-Criteria Decision Analysis in Predictive ModelingPetr Silhavy0https://orcid.org/0000-0002-3724-7854Katerina Hlavackova-Schindler1https://orcid.org/0000-0001-6467-3077Radek Silhavy2https://orcid.org/0000-0002-5637-8796Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech RepublicData Mining and Machine Learning Research Group, Faculty of Computer Science, University of Vienna, Vienna, AustriaFaculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech RepublicAccurate model selection is essential in predictive modelling across various domains, significantly impacting decision-making and resource allocation. Despite extensive research, the model selection process remains challenging. This work aims to integrate the Minimum Description Length principle with the Multi-Criteria Decision Analysis to enhance the selection of forecasting machine learning models. The proposed MDL-MCDA framework combines the MDL principle, which balances model complexity and data fit, with the MCDA, which incorporates multiple evaluation criteria to address conflicting error measurements. Four datasets from diverse domains, including software engineering (effort estimation), healthcare (glucose level prediction), finance (GDP prediction), and stock market prediction, were used to validate the framework. Various regression models and feed-forward neural networks were evaluated using criteria such as MAE, MAPE, RMSE, and Adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. We employed the Analytic Hierarchy Process (AHP) to determine the relative importance of these criteria. We conclude that the integration of MDL and MCDA significantly improved model selection across all datasets. The cubic polynomial regression model and the multi-layer perceptron models outperformed other models in terms of AHP score and MDL criterion. Specifically, the MDL-MCDA approach provided a more nuanced evaluation, ensuring the selected models effectively balanced complexity and predictive accuracy.https://ieeexplore.ieee.org/document/10849532/Multicriteria decision analysisminimum model lengthmachine learningmodel selection predictionMDL-MCDA |
| spellingShingle | Petr Silhavy Katerina Hlavackova-Schindler Radek Silhavy Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling IEEE Access Multicriteria decision analysis minimum model length machine learning model selection prediction MDL-MCDA |
| title | Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling |
| title_full | Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling |
| title_fullStr | Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling |
| title_full_unstemmed | Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling |
| title_short | Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling |
| title_sort | minimum description length and multi criteria decision analysis in predictive modeling |
| topic | Multicriteria decision analysis minimum model length machine learning model selection prediction MDL-MCDA |
| url | https://ieeexplore.ieee.org/document/10849532/ |
| work_keys_str_mv | AT petrsilhavy minimumdescriptionlengthandmulticriteriadecisionanalysisinpredictivemodeling AT katerinahlavackovaschindler minimumdescriptionlengthandmulticriteriadecisionanalysisinpredictivemodeling AT radeksilhavy minimumdescriptionlengthandmulticriteriadecisionanalysisinpredictivemodeling |