# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## 5 Experimental Results
### Predicting Results of Employment Stability  
In this subsection, to investigate the impact of career decisions, we evaluated CBBOA-SVM with feature selection (CBBOA-SVM-FS) with some real datasets collected. The results of the evaluation using accuracy (ACC), Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity are given in Table 2. The ACC of a model is defined as the proportion of properly categorized events out of all classified events, and it represents the model's performance in categorizing the information. Specificity is a performance metric used to evaluate the ability of a binary classification model to distinguish between normal and abnormal cases. The sensitivity of the binary classification model is used to evaluate the metrics of the model in terms of spotting aberrant data. In order to properly examine the effectiveness of the classification model, the MCC is utilized. This provides a more objective predictive evaluation than just percentile rankings. Among the results given, the ACC result for CBBOA-SVM-FS is 94.2%, the MCC result is 88.9%, the Sensitivity result is 94.5%, and the Specificity result is 94%. The analysis of the results obtained by  
Figure 6: Balance analysis of CBBOA and BOA. CBBOA-SVM-FS through these four evaluation metrics fully illustrates the feasibility of using CBBOA-SVM-FS to study the impact of career decisions for college students. To further validate the performance of CBBOA-SVM-FS, we compared it with five other similar methods, namely CBBOA-SVM, BOA-SVM, ANN, RF, and KELM. The evaluation results of ACC, sensitivity, specificity, and MCC for each method are shown in Figure 8. For the most important one, ACC, CBBOA-SVM-FS obtained a high result of 94.20%, which is 1.90% better than CBBOA-SVM, 2.40% better than BOA-SVM, 8.80% better than ANN, 2.90% better than RF, and 4.90% better than KELM. Therefore, CBBOA-SVM-FS is the best among the performance of all the methods involved in the comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **ACC** & **MCC** & **Sensitivity** & **Specificity** \\ \hline Num.1 & 0.950 & 0.905 & 0.900 & 1.000 \\ Num.2 & 0.952 & 0.909 & 0.909 & 1.000 \\ Num.3 & 0.952 & 0.908 & 1.000 & 0.900 \\ Num.4 & 0.952 & 0.909 & 0.909 & 1.000 \\ Num.5 & 0.952 & 0.908 & 1.000 & 0.900 \\ Num.6 & 0.857 & 0.716 & 0.909 & 0.800 \\ Num.7 & 0.905 & 0.826 & 0.818 & 1.000 \\ Num.8 & 0.950 & 0.905 & 1.000 & 0.900 \\ Num.9 & 0.950 & 0.905 & 1.000 & 0.900 \\ Num.10 & 1.000 & 1.000 & 1.000 \\
**AVG** & 0.942 & 0.889 & 0.945 & 0.940 \\
**STD** & 0.037 & 0.074 & 0.064 & 0.070 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of CBBOA-SVM-FS in the light of four metrics. Figure 7: Diversity analysis of CBBOA and BOA. Moreover, in terms of the variance of the ACC obtained by the methods involved in the comparison, CBBOA-SVM-FS is 0.037, CBBOA-SVM is 0.076, BOA-SVM is 0.071, ANN is 0.095, RF is 0.083, and KELM is 0.075. By comparing the variance, we can find that the stability of CBBOA-SVM-FS is also better with respect to ACC. In terms of sensitivity, CBBOA-SVM obtains the best result of 98.20%, and CBBOA-SVM-FS is the second-best, with a result of 94.50%. In addition, the results of other methods are 93.30% for BOA-SVM, 92.60% for RF, 87.70% for KELM, and 78.20% for ANN, respectively, which shows that the proposed methods CBBOA-SVM-FS and CBBOA-SVM are also superior to other methods in terms of Sensitivity. Further, the results in terms of variance also show that CBBOA-SVM-FS and CBBOA-SVM are more stable than the other methods. When evaluated using Specificity, CBBOA-SVM-FS is 94%, which is 8.00% better than CBBOA-SVM, 4.00% better than BOA-SVM, 1.00% better than ANN, 4.00% better than RF, and 3.00% better than KELM, showing that CBBOA-SVM-FS is the best. Moreover, on top of stability, CBBOA-SVM-FS is also better than CBBOA-SVM, BOA-SVM, RF, and KELM. In the MCC results, CBBOA-SVM-FS is the best with 88.90%.