Abstract
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.
Highlights
IntroductionA considerable amount of research in the field of machine learning (ML) is concerned with developing methods that automate classification tasks [1]
The results indicate that the enhanced firefly algorithm (FA) yielded all of the optimal values, which were very close to the analytically obtained values
The performance of the proposed classification system was evaluated in terms of accuracy, precision, sensitivity, specificity and area under the curve (AUC)
Summary
A considerable amount of research in the field of machine learning (ML) is concerned with developing methods that automate classification tasks [1]. Classification tasks are involved in several real-world applications, in such fields as civil engineering [2,3], medicine [4], land use [5], energy [6], investment [7], and marketing [8]. It is obvious that problems in the engineering domain are multi-class issues. There is a need to establish a learning framework for solving multi-level classification problems efficiently and effectively, which is the primary purpose of this study
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