Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs
Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/16/2548 |
| _version_ | 1797410574841151488 |
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| author | Bayu Adhi Tama Marco Comuzzi |
| author_facet | Bayu Adhi Tama Marco Comuzzi |
| author_sort | Bayu Adhi Tama |
| collection | DOAJ |
| description | Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article, an advanced stacking ensemble technique for outcome-based predictive monitoring is introduced. The proposed stacking ensemble employs strong learners as base classifiers, i.e., other ensembles. More specifically, we consider stacking of random forests, extreme gradient boosting machines, and gradient boosting machines to train a process outcome prediction model. We evaluate the proposed approach using publicly available event logs. The results show that the proposed model is a promising approach for the outcome-based prediction task. We extensively compare the performance differences among the proposed methods and the base strong learners, using also statistical tests to prove the generalizability of the results obtained. |
| first_indexed | 2024-03-09T04:32:10Z |
| format | Article |
| id | doaj.art-a2edd39542a9402786e19f0d38b0252a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:32:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a2edd39542a9402786e19f0d38b0252a2023-12-03T13:34:22ZengMDPI AGElectronics2079-92922022-08-011116254810.3390/electronics11162548Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event LogsBayu Adhi Tama0Marco Comuzzi1Department of Information Systems, University of Maryland, Baltimore County (UMBC), MD 21250, USADepartment of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, KoreaOutcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article, an advanced stacking ensemble technique for outcome-based predictive monitoring is introduced. The proposed stacking ensemble employs strong learners as base classifiers, i.e., other ensembles. More specifically, we consider stacking of random forests, extreme gradient boosting machines, and gradient boosting machines to train a process outcome prediction model. We evaluate the proposed approach using publicly available event logs. The results show that the proposed model is a promising approach for the outcome-based prediction task. We extensively compare the performance differences among the proposed methods and the base strong learners, using also statistical tests to prove the generalizability of the results obtained.https://www.mdpi.com/2079-9292/11/16/2548ensemble learningevent logsstackingprocess monitoring |
| spellingShingle | Bayu Adhi Tama Marco Comuzzi Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs Electronics ensemble learning event logs stacking process monitoring |
| title | Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs |
| title_full | Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs |
| title_fullStr | Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs |
| title_full_unstemmed | Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs |
| title_short | Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs |
| title_sort | leveraging a heterogeneous ensemble learning for outcome based predictive monitoring using business process event logs |
| topic | ensemble learning event logs stacking process monitoring |
| url | https://www.mdpi.com/2079-9292/11/16/2548 |
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