# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 6 Conclusions and Recommendation  
This work proposed a hybrid inference model that integrated an enhanced firefly algorithm (enhanced FA) with a least squares support vector machine (LSSVM) model and decomposition strategy (i.e., one-against-one, OAO) to improve its predictive accuracy in solving multi-level classification problems. The proposed system provides a baseline classification model, called OAO-LSSVM. The effectiveness of the enhanced FA Optimized-OAO-LSSVM model is compared with that of the baseline OAO-LSSVM model. To verify the applicability and efficiency of the proposed model in solving multi-level classification problems, the predictive performance of the model was compared to other multi-classification methods and prior studies with respect to accuracy, precision, sensitivity, specificity and AUC. Three case studies, involving the multi-class problems of categorizing steel plate faults, assessing the water quality in a reservoir, and managing the condition of urban land cover, were considered. The proposed model exhibited higher predictive accuracy than the baseline model (OAO-LSSVM), experimental studies and other single multi-class algorithms with the highest accuracy in each case. In particular, the proposed model yielded 91.085%, 93.650% and 87.274% accuracy in steel plate faults, water quality in a reservoir, and urban land cover, respectively. Therefore, the model can be used as a decision-making tool in solving practical problems in the fields of civil engineering and construction management. A main contribution of this work is the extension of a binary-class model to a meta-heuristically optimized multi-level model for efficiently and effectively solving classification problems involving multi-class data. Another major contribution is the design of an intelligent computing system for users with ease that was proved to be an effective project management software. Although the proposed model exhibited excellent predictive accuracy, and a graphical user interface was effectively implemented, it has limitations that should be addressed by future studies. The proposed model does not have high predictive accuracy when applied to small datasets or the unbalanced numbers of data points. Future studies should also improve the model to make it useful for solving multiple inputs and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Performance Measure**} \\ \cline{2-6}  & **Accuracy (\%)** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline Dataset 1—Diagnosis of faults in steel plates & & & & & \\ Original value & 91.085 & 89.995 & 90.437 & 91.020 & 0.907 \\ Feature scaling & 88.646 & 86.518 & 88.458 & 88.620 & 0.885 \\ \hline Dataset 2—Quality of water in reservoir & & & & & \\ Original value & 93.526 & 92.335 & 94.272 & 93.622 & 0.939 \\ Feature scaling & 93.650 & 92.531 & 93.840 & 93.746 & 0.938 \\ \hline Dataset 3—Urban land cover & & & & & \\ Original value & 87.274 & 87.048 & 89.918 & 87.297 & 0.886 \\ Feature scaling & 86.521 & 86.003 & 87.310 & 86.534 & 0.874 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Analytical results obtained using Optimized-OAO-LSSVM. multiple outputs of multiclass classification problems, and develop it in a cloud computing environment to increase its ubiquitous applicability. Conceptualization, J.-S.C.; data curation, T.T.P.P. and C.-C.-H.; formal analysis, J.-S.C. and T.T.P.P. ; funding acquisition, J.-S.C.; investigation, J.-S.C. and T.T.P.P. ; methodology, J.-S.C. and T.T.P.P. ; project administration, J.-S.C. and C.-C.-H.; resources, J.-S.C. and C.-C.H. ; software, J.-S.C. and T.T.P.P. ; supervision, J.-S.C.; validation, J.-S.C., T.T.P.P. and C.-C.-H.; visualization, J.-S.C. and T.T.P.P. ; writing--original draft, J.-S.C. and T.T.T.P.P. ; writing--review and editing, J.-S.C. and T.T.P.P. All authors have read and agreed to the published version of the manuscript.