# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 3 Results and Discussion  
### Comparison of Different Feature Extraction and Classification Methods  
According to the previous article, this study compared the prediction effects of the three classifiers: Naive Bayes, SVM, and multilayer perceptron. Among them, the parameters of the three classifiers adopted the default parameters built in the algorithm. The default kernel function of SVM was linear kernel function, and the value of penalty coefficient was C = 1.0. The topology of the multilayer perceptron was selected as a simple 3-layer network, including an input layer, a hidden layer, and an output layer, using the Sigmoid function as the activation function. CC-PSSM and monoTriKap feature extraction methods were compared with previous research results, and we tested the accuracy of the classification of the immunoglobulin dataset through 5-fold cross validation [73]. The predictions obtained from the 760 features extracted by the CC-PSSM method, and the 212 best feature subsets generated by the monoTriK Gap method with the three different classifiers mentioned above, are presented in Table 2, and the contrasts of ACC values are presented in Figure 4.  
The data in Table 2 show that for the CC-PSSM feature extraction method, the ACC values of the multilayer perceptron classifier are higher than those of Naive Bayes and SVM. Using multilayer perceptron to predict the immunoglobulin TPR value, the value reached 0.961, the FPR value reached 0.041, the MCC value reached 0.921, and the ACC value reached 96.0526%. The ROC curve area was 0.994. Compared with the Naive Bayes \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Classifier** & **TPR** & **FPR** & **Precision** & **MCC** & **auROC** & **ACC** \\ \hline \multirow{3}{*}{CC-PSSM} & NB & 0.930 & 0.072 & 0.930 & 0.860 & 0.951 & 0.9298 \\  & MLP & 0.961 & 0.041 & 0.961 & 0.921 & 0.994 & 0.9605 \\  & SVM & 0.952 & 0.050 & 0.952 & 0.904 & 0.951 & 0.9517 \\ \hline \multirow{3}{*}{monoTriK Gap} & NB & 0.921 & 0.081 & 0.921 & 0.842 & 0.951 & 0.9211 \\  & MLP & 0.987 & 0.013 & 0.987 & 0.974 & 0.997 & 0.9868 \\  & SVM & 0.996 & 0.004 & 0.996 & 0.991 & 0.996 & 0.9956 \\ \hline [PERSON] et al. [29] & SVM & 0.963 & 0.025 & \(\backslash\) & \(\backslash\) & 0.994 & 0.9690 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compare the results of different feature methods under different classifiers. Figure 4: The comparison result of the evaluation index. Comparison of the ACC values between CC–PSSM and monoTriK Gap feature representation methods under different classifiers. classifier, the ACC value increased by 3.1%. For the monoTriKGap feature extraction method, the TPR, precision, ACC, and other values of the SVM classifier were higher than the values of Naive Bayes and multilayer perceptron. Using SVM to predict the immunoglobulin TPR value reached 0.996, the FPR value reached 0.004, the MCC value reached 0.991, and the ACC value reached 99.5614%. The ROC curve area was 0.996. Compared with the Naive Bayes classifier, the ACC value increased by 7.5%. Through comparison and analysis, the study found that the SVM classification result of the best feature subset extracted by monoTriKGap improved compared with the multilayer perceptron classification result of the feature subset extracted by CC-PSSM and the prediction model proposed by [PERSON] et al. [29]. This shows that employing the monoTriKGap feature extraction method to generate the best feature subset and SVM can achieve a higher prediction effect, which is most conducive to the accurate prediction of immunoglobulins. Then, we compared the performance of SVM under three kinds of kernel functions (linear kernel function, quadratic polynomial kernel function, and radial basis kernel function), as shown in Table 3.  
It can be seen from Table 3 that when using the best feature subset of monoTriKGap, the prediction effect of the linear kernel function was better than the other two kernel functions.