# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 3 Results and Discussion
### Machine Learning Classification  
Table 2 shows the confusion matrix and classification performance of the top 20 genes selected based on IG. Accuracy as well as eight further measurements are presented in Table 2. Different classifiers were used to evaluate the performance of the 20 selected genes based on the IG algorithm, resulting in relatively high accuracy. The accuracy ranged from 95.22% related to logistic and SMO classifiers to 97.91% for random forest. The average accuracy considering all five classifiers stands to be 96.24%. Moreover, the relative efficiency of random forest over other classifiers could be reflected by considering all performance parameters (Table 2). Despite similar results for logistic and SMO for TP rate, FP rate, precision, recall, F-measure, and MCC parameters, the logistic algorithm provided a better ROC and PRC area, demonstrating better performance than SMO (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Predicted Class**} & \multirow{2}{*}{**Actual Class**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**TP**} & \multicolumn{2}{c}{**FP Rate**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} & \multicolumn{2}{c}{**ROC Area**} & \multicolumn{2}{c}{**PRC Area**} \\ \cline{2-2} \cline{6-11}  & **Control** & & & & & & & & & & & \\ \hline BayesNet & 155 & 5 & control & 96.42 & 0.964 & 0.035 & 0.964 & 0.964 & 0.928 & 0.993 & 0.994 \\  & 7 & 168 & stress & & & & & & & & \\ \hline Logistic & 155 & 5 & control & 95.22 & 0.952 & 0.046 & 0.953 & 0.952 & 0.952 & 0.905 & 0.987 & 0.979 \\  & 11 & 164 & stress & & & & & & & & \\ \hline Multilayer & 158 & 2 & control & 96.42 & 0.964 & 0.034 & 0.965 & 0.964 & 0.964 & 0.929 & 0.994 & 0.994 \\  & 10 & 165 & stress & & & & & & & & \\ \hline SMO & 154 & 6 & control & 95.22 & 0.952 & 0.047 & 0.953 & 0.952 & 0.952 & 0.905 & 0.953 & 0.931 \\  & 10 & 165 & stress & & & & & & & & \\ \hline Random & 157 & 3 & control & 97.91 & 0.979 & 0.021 & 0.979 & 0.979 & 0.979 & 0.958 & 0.993 & 0.999 \\  & 4 & 171 & stress & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrices and discrimination performance of Arabidopsis on expression levels of 20 selected differentially expressed genes (DEGs) under control and stress conditions based on the information gain (IG) feature selection algorithm.  
Figure 2: **(a)** The LASSO model: each curve corresponds to a variable. It shows the path of its coefficient against the L1 norm of the whole coefficient vector as \(\lambda\) varies. The axis above the graph indicates the number of non-zero coefficients at the current \(\lambda\), which is LASSO’s effective degree of freedom (df). **(b)** LASSO model identified 42 genes that provide the most regularized model such that the cross-validated error is within one standard error of the minimum.  
In the ReliefF selection method, the overall accuracy was 97.016% (Table 3), which was higher than the accuracy obtained by the IG algorithm. Random forest and multilayer perceptron had the highest and similar accuracy ratings of 98.51%, while the minimum accuracy was obtained by BayesNet (94.93%). Overall, random forest performed slightly better considering all parameters together (Table 3).  
Random forest was the best-performing classifier among all of the classifiers tested, providing the highest accuracy and other tested evaluation metrics for both ReliefF and IG (Tables 2 and 3). The effectiveness of random forest may be due to its ability to handle large datasets with many variables and automatically balance datasets, making it suitable for complex tasks [31], as previously demonstrated in other studies [32].