## 5 Experimentation Part Text-Mining
### Proposed Multi-Objective Evolutionary Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Decision Tree (J48)**} \\ \hline
**TP Rate** & **FP Rate** & **Precession** & **Recall** & **F-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline
0.400 & 0.000 & 1.000 & 0.400 & 0.571 & 0.604 & 0.881 & 0.694 & All types \\
1.000 & 0.033 & 0.857 & 1.000 & 0.923 & 0.910 & 0.983 & 0.857 & Bevel gear \\
1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & Helical gear \\
1.000 & 0.071 & 0.800 & 1.000 & 0.889 & 0.862 & 0.991 & 0.950 & Spur gear \\
1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & Worm gear \\
0.917 & 0.021 & 0.932 & 0.917 & 0.903 & 0.899 & 0.979 & 0.923 & Weighted Avg. \\ \hline \multicolumn{8}{c}{**Naïve Bayes**} \\ \hline
**TP Rate** & **FP Rate** & **Precession** & **Recall** & **F-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline
0.200 & 0.097 & 0.250 & 0.200 & 0.222 & 0.114 & 0.490 & 0.181 & All types \\
0.833 & 0.033 & 0.833 & 0.833 & 0.833 & 0.800 & 0.978 & 0.897 & Bevel gear \\
0.500 & 0.125 & 0.333 & 0.500 & 0.400 & 0.316 & 0.672 & 0.573 & Helical and worm gear \\
1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & Helical gear \\
0.875 & 0.000 & 1.000 & 0.875 & 0.933 & 0.919 & 0.938 & 0.920 & Spur gear \\
0.714 & 0.069 & 0.714 & 0.714 & 0.714 & 0.645 & 0.808 & 0.723 & Worm gear \\
0.722 & 0.046 & 0.738 & 0.722 & 0.727 & 0.681 & 0.838 & 0.750 & Weighted Avg. \\ \hline \multicolumn{8}{c}{**Random Forest**} \\ \hline
**TP Rate** & **FP Rate** & **Precession** & **Recall** & **F-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline
0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.771 & 0.435 & All types \\
1.000 & 0.067 & 0.750 & 1.000 & 0.857 & 0.837 & 1.000 & 1.000 & Bevel gear \\
0.500 & 0.063 & 0.500 & 0.500 & 0.500 & 0.438 & 0.969 & 0.817 & Helical and worm gear \\
1.000 & 0.033 & 0.857 & 1.000 & 0.923 & 0.910 & 1.000 & 1.000 & Helical gear \\
0.875 & 0.036 & 0.875 & 0.875 & 0.875 & 0.839 & 0.996 & 0.986 & Spur gear \\
1.000 & 0.069 & 0.778 & 1.000 & 0.875 & 0.851 & 1.000 & 1.000 & Worm gear \\
0.778 & 0.045 & 0.669 & 0.778 & 0.717 & 0.692 & 0.964 & 0.898 & Weighted Avg. \\ \hline \multicolumn{8}{c}{**Support Vector Machines**} \\ \hline
**TP Rate** & **FP Rate** & **Precession** & **Recall** & **F-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline
0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.500 & 0.139 & All types \\
0.667 & 0.033 & 0.800 & 0.667 & 0.727 & 0.683 & 0.817 & 0.589 & Bevel gear \\
0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.500 & 0.111 & Helical and worm gear \\
0.500 & 0.000 & 1.000 & 0.500 & 0.667 & 0.674 & 0.750 & 0.583 & Helical gear \\
0.875 & 0.286 & 0.467 & 0.875 & 0.609 & 0.497 & 0.795 & 0.436 & Spur gear \\
0.857 & 0.241 & 0.462 & 0.857 & 0.600 & 0.507 & 0.808 & 0.423 & Worm gear \\
0.483 & 0.093333 & 0.454833 & 0.483 & 0.433 & 0.3935 & 0.695 & 0.380 & Weighted Avg. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Various performance measures for machine-learning algorithms Decision Tree (J48), Naive Bayes, Random Forest, and Support Vector Machines. Figure 6: Screen shot of extracted enterprise information and classification into task-specific supplier with text mining. Figure 7: Flowchart of the proposed HMFO. **Step 1. **: In HMFO, potential solutions are represented as moths and variables are represented as position in the moth space. A matrix consists of all the moths (\(n\)), and their dimension is d.  
\[L=\left[ \begin{array}{cccc}L_{11}&L_{12}&\dots&L_{1d}\\ L_{21}&L_{22}&\dots&L_{2d}\\ L_{31}&L_{32}&\dots&L_{3d}\\ L_{41}&L_{42}&\dots&L_{4d}\end{array} \right]\]  
Initialization of moth population and their spaces are defined with the time matrices and their corresponding inputs. In this proposed HMFEO, a new type of encoding schema was presented to suit the problem nature. The encoding scheme for makespan is presented in Figure 8.  
The example of encoding schema is represented in Figure 8, and the encoding consists of three parts.