# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 5 Engineering Applications
### Multi-Level Problems
#### 5.2.2 Case 2--Quality of Water in Reservoir
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Empirical Models**} & \multicolumn{4}{c}{**Performance Measure**} & \multicolumn{2}{c}{**Improved Accuracy by Optimized-OAO-LSSVM**} \\  & \multicolumn{2}{c}{**Reported in Primary**} & \multicolumn{2}{c}{**Performance Measure**} & \multicolumn{2}{c}{**Accuracy by Optimized-OAO-LSSVM**} \\
**Dataset** & \multicolumn{2}{c}{**Multi-Class Models**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Specificity**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**OAO-LSSVM**} \\  & \multicolumn{2}{c}{} & **(\%)** & & & & & \\ \hline \multirow{9}{*}{Dataset} & SMO & 86.357 & 86.400 & 86.300 & 95.300 & 0.908 & 5.191 \\  & Multiclass Classifier & 85.726 & 85.700 & 85.600 & 96.000 & 0.908 & 5.884 \\  & Naive Bayes & 82.334 & 82.300 & 84.440 & 95.960 & 0.902 & 9.608 \\  & Logistic & 86.124 & 86.100 & 86.000 & 97.400 & 0.917 & 5.447 \\
1—Diagnosis & LibSVM & 31.704 & 31.700 & 10.100 & 89.900 & 0.500 & 65.193 \\  & GS-SVM [65] & 77.800 & - & - & - & - & 14.586 \\ steel plates & GA-SVM [65] & 78.000 & - & - & - & - & 14.366 \\  & PSO-SVM [65] & 79.600 & - & - & - & - & 12.610 \\  & OAO-LSSVM & 53.553 & 28.764 & - & 59.148 & - & 41.206 \\  & Optimized-OAO-LSSVM & 91.085 & 89.995 & 90.437 & 91.020 & 0.907 & - \\ \hline \multirow{9}{*}{Dataset} & SMO & 75.238 & 75.200 & 77.500 & 85.900 & 0.817 & 19.661 \\  & Multiclass Classifier & 85.397 & 85.400 & 86.500 & 94.900 & 0.907 & 8.813 \\  & Naive Bayes & 76.000 & 76.000 & 78.700 & 99.500 & 0.891 & 18.847 \\
2—Quality of water in & Logistic & 89.580 & 89.600 & 89.600 & 95.000 & 0.923 & 4.346 \\ reservoir & LibSVM & 80.950 & 81.000 & 81.000 & 87.600 & 0.843 & 13.561 \\  & OAO-LSSVM & 92.196 & 90.794 & 90.633 & 92.078 & 0.914 & 1.553 \\  & Optimized-OAO-LSSVM & 93.650 & 92.531 & 93.840 & 93.746 & 0.938 & - \\ \hline \multirow{9}{*}{Dataset} & SMO & 85.778 & 85.800 & 86.000 & 89.000 & 0.875 & 1.714 \\  & Multiclass Classifier & 64.900 & 64.900 & 64.800 & 99.400 & 0.821 & 25.636 \\  & Naive Bayes & 81.000 & 81.000 & 81.600 & 91.800 & 0.867 & 7.189 \\ \cline{1-1}  & Logistic & 65.926 & 65.900 & 65.900 & 95.300 & 0.806 & 24.461 \\ \cline{1-1}  & LibSVM & 18.370 & 18.400 & 19.000 & 81.400 & 0.502 & 78.951 \\ \cline{1-1}  & k-NN classifier [79] & 80.140 & - & - & - & - & 8.174 \\ \cline{1-1}  & ELM classifier [79] & 84.700 & - & - & - & - & 2.949 \\ \cline{1-1}  & SVM classifier [79] & 84.890 & - & - & - & - & 2.732 \\ \cline{1-1}  & OAO-LSSVM & 18.378 & 11.637 & - & - & - & 78.942 \\ \cline{1-1}  & Optimized-OAO-LSSVM & 87.274 & 87.048 & 89.918 & 87.297 & 0.886 & - \\ \cline{1-1}  & LSSVM & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of performance measures and rates of improved accuracy achieved by Optimized-OAO-LSSVM. Table 7 compares the performances of the SMO, Multiclass Classifier, Naive Bayes, Logistic, LibSVM, OAO-LSSVM and Optimized-OAO-LSSVM models when used to predict the quality of water in a reservoir, using test data. The numerical results revealed that the Optimized-OAO-LSSVM is the best model for predicting this dataset in terms of accuracy, precision, sensitivity, specificity and AUC value (93.650% 92.531%, 93.840%, 93.746% and 0.938 respectively). Moreover, the hypothesis tests concerning accuracy established that the Optimized-OAO-LSSVM model was more efficient than the other models at a significance level of 1%.