Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classifica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/12/5533 |
| _version_ | 1797530093244907520 |
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| author | Jui-Sheng Chou Trang Thi Phuong Pham Chia-Chun Ho |
| author_facet | Jui-Sheng Chou Trang Thi Phuong Pham Chia-Chun Ho |
| author_sort | Jui-Sheng Chou |
| collection | DOAJ |
| description | Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting in civil and construction engineering. The proposed system integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO) method, and the least squares support vector machine (LSSVM). The enhanced FA automatically fine-tunes the hyperparameters of the LSSVM to construct an optimized LSSVM classification model. Ten benchmark functions are used to evaluate the performance of the enhanced optimization algorithm. Two binary-class datasets related to geotechnical engineering, concerning seismic bumps and soil liquefaction, are then used to clarify the application of the proposed system to binary problems. Further, this investigation uses multi-class cases in civil engineering and construction management to verify the effectiveness of the model in the diagnosis of faults in steel plates, quality of water in a reservoir, and determining urban land cover. The results reveal that the system predicts faults in steel plates with an accuracy of 91.085%, the quality of water in a reservoir with an accuracy of 93.650%, and urban land cover with an accuracy of 87.274%. To demonstrate the effectiveness of the proposed system, its predictive accuracy is compared with that of a non-optimized baseline model, single multi-class classification algorithms (sequential minimal optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the library support vector machine (LibSVM) and logistic regression) and prior studies. The analytical results show that the proposed system is promising project analytics software to help decision makers solve multi-level classification problems in engineering applications. |
| first_indexed | 2024-03-10T10:24:02Z |
| format | Article |
| id | doaj.art-08341147c31d415a9cac9293f996c5a3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:24:02Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-08341147c31d415a9cac9293f996c5a32023-11-22T00:10:31ZengMDPI AGApplied Sciences2076-34172021-06-011112553310.3390/app11125533Metaheuristic Optimized Multi-Level Classification Learning System for Engineering ManagementJui-Sheng Chou0Trang Thi Phuong Pham1Chia-Chun Ho2Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106335, TaiwanDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106335, TaiwanDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106335, TaiwanMulti-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting in civil and construction engineering. The proposed system integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO) method, and the least squares support vector machine (LSSVM). The enhanced FA automatically fine-tunes the hyperparameters of the LSSVM to construct an optimized LSSVM classification model. Ten benchmark functions are used to evaluate the performance of the enhanced optimization algorithm. Two binary-class datasets related to geotechnical engineering, concerning seismic bumps and soil liquefaction, are then used to clarify the application of the proposed system to binary problems. Further, this investigation uses multi-class cases in civil engineering and construction management to verify the effectiveness of the model in the diagnosis of faults in steel plates, quality of water in a reservoir, and determining urban land cover. The results reveal that the system predicts faults in steel plates with an accuracy of 91.085%, the quality of water in a reservoir with an accuracy of 93.650%, and urban land cover with an accuracy of 87.274%. To demonstrate the effectiveness of the proposed system, its predictive accuracy is compared with that of a non-optimized baseline model, single multi-class classification algorithms (sequential minimal optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the library support vector machine (LibSVM) and logistic regression) and prior studies. The analytical results show that the proposed system is promising project analytics software to help decision makers solve multi-level classification problems in engineering applications.https://www.mdpi.com/2076-3417/11/12/5533machine learningmulti-level classificationmetaheuristic optimizationswarm and evolutionary algorithmchaotic maps and Lévy flightshybrid computing system |
| spellingShingle | Jui-Sheng Chou Trang Thi Phuong Pham Chia-Chun Ho Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management Applied Sciences machine learning multi-level classification metaheuristic optimization swarm and evolutionary algorithm chaotic maps and Lévy flights hybrid computing system |
| title | Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management |
| title_full | Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management |
| title_fullStr | Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management |
| title_full_unstemmed | Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management |
| title_short | Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management |
| title_sort | metaheuristic optimized multi level classification learning system for engineering management |
| topic | machine learning multi-level classification metaheuristic optimization swarm and evolutionary algorithm chaotic maps and Lévy flights hybrid computing system |
| url | https://www.mdpi.com/2076-3417/11/12/5533 |
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