# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 5 Engineering Applications
### Multi-Level Problems
#### 5.2.1 Case 1--Diagnosis of Faults in Steel Plates  
Fault diagnosis is important in industrial production. For instance, producing defective products can impose a high cost on a manufacturer of steel products. Therefore, in this investigation a dataset of faults in steel plates, which are important raw materials in hundreds of industrial products, is used as a practical case. The original dataset was obtained from Semeion, Research of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy. In this dataset, faults in steel plates are classified into 7 types, including Pastry, Zscratch, Kscratch, Stains, Dirtiness, Bumps and Other. The database contains 1941 data points with 27 independent variables. To prevent confusion in multi-class classification, [PERSON] et al. [65] eliminated faults of class 7 because that class did not refer to a particular kind of fault. Furthermore, to improve predictive accuracy, they used the recursive feature elimination (RFE) algorithm to reduce the number of dimensions of the multi-classification. Therefore, [PERSON] et al. used a modified steel plates fault dataset (1268 samples) with 20 independent attributes and six types of fault [65]. To obtain a fair comparison, therefore, the proposed model was applied to the modified data. Table 6 presents the inputs and profile of categorical labels for data concerning faults in steel plates. \begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Cross-Fold Validation** & **Accuracy (\%)** & **Training and Test Time (s)** \\ \hline Dataset 1—Seismic bumps (2584 samples) & & & \\ SFA-LSSVM (original value) & 10 & 93.46 & 355,913.59 \\ SFA-LSSVM (feature scaling) & 10 & 93.96 & 174,328.48 \\ Optimized-OAO-LSSVM (original value) & 10 & 93.42 & 1136.60 \\ Optimized-OAO-LSSVM (feature scaling) & 10 & 93.30 & 717.37 \\ \hline Dataset 2—Soil liquefaction (226 samples) & & & \\ SFA-LSSVM (original value) & 10 & 94.31 & 19,884.82 \\ SFA-LSSVM (feature scaling) & 10 & 95.18 & 998.45 \\ Optimized-OAO-LSSVM (original value) & 10 & 93.38 & 57.22 \\ Optimized-OAO-LSSVM (feature scaling) & 10 & 92.93 & 56.14 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of performances of SFA-LSSVM and Optimized-OAO-LSSVM models used to solve binary problems. Accuracy, precision, sensitivity, specificity and AUC are indices used to evaluate the effectiveness of the proposed model. High values indicate favorable performance and vice versa. Accuracy is the most commonly used index. Table 7 presents the predictive performances of SMO, the Multiclass Classifier, the Naive Bayes, Logistic, LibSVM and several empirical models [65], and the OAO-LSSVM and Optimized-OAO-LSSVM models when applied to the steel fault dataset. [PERSON] et al. used three optimizing algorithms--grid search (GS), GA and PSO--combined with SVM to improve the accuracy of classification in the steel fault dataset [65]. They showed that the SVM model, optimized by PSO, was the best for predicting the test data, with an accuracy of 79.6%. With the same data, the Optimized-OAO-LSSVM had an accuracy of 91.085%. The Optimized-OAO-LSSVM model was more accurate than SMO (86.357%), the Multiclass Classifier (85.726%), the Naive Bayes (82.334%), the Logistic model (86.124%), the LibSVM (31.704%) and the OAO-LSSVM model (53.553%). The statistical accuracy of the Optimized-OAO-LSSVM model, applied to the test data, was better than those of other algorithms at a significance level of 1%.