Multi-class classification is one of the major challenges in machine learning and an on-going 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 model for forecasting in engineering problems. The proposed model integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO), 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. The developed model is called the Optimized-OAO-LSSVM. 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 model 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 revealed that the Optimized-OAO-LSSVM model predicted 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 model, its predictive accuracy was compared with that of a non-optimized baseline model (OAO-LSSVM), single multi-class classification algorithms (Sequential Minimal Optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the Library Support vector machine (LibSVM) and Logistic). The analytical results showed that the proposed model is a promising tool to help decision-makers solve classification problems in civil engineering and construction management.

Multi-class classification is one of the major challenges in machine learning and an on-going 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 model for forecasting in engineering problems. The proposed model integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO), 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. The developed model is called the Optimized-OAO-LSSVM. 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 model 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 revealed that the Optimized-OAO-LSSVM model predicted 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 model, its predictive accuracy was compared with that of a non-optimized baseline model (OAO-LSSVM), single multi-class classification algorithms (Sequential Minimal Optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the Library Support vector machine (LibSVM) and Logistic). The analytical results showed that the proposed model is a promising tool to help decision-makers solve classification problems in civil engineering and construction management.

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