*4.5. Classification*

We performed a comprehensive set of experiments to test and validate our proposed model using five classifiers, namely, KNN, NB, SVM, LR, and MLP. These classifiers were used with alpha asymmetry, beta, and gamma waves from channel *AF*3 as features to classify long-term stress. Each combination of the selected features was analyzed with each of the classifiers. The results of these classifiers in terms of average accuracy are shown in Table 5. We used 10-fold cross validation in these experiments since our dataset was limited. We used 10 folds, where in each fold 90% of the data were used for training and 10% for testing and reporting the average values of parameters across all 10 folds. The hyper parameters for classifiers used in our experiment were chosen using a grid search.

We observed that the classifier accuracy was high whenever alpha asymmetry was either used as a single feature or in combination with other features. The SVM- and LR-based classifiers give the highest accuracy when alpha asymmetry was used as a feature. The performance evaluation parameters for these classifiers are given in Table 6. We also observed that both SVM and LR show very similar values for kappa statistic and F-measure. SVM may have a slightly lesser mean absolute error of 0.15 than that of logistic regression with a value of 0.22, whereas LR has a lesser RMAE of 0.36 than that of SVM i.e., 0.38. The overall classification accuracy of both these classifier is similar. Overall, we concluded that SVM may be a better choice for an assisting system for stress recognition.


**Table 5.** Accuracy of classifiers for various combinations of statistically significant features.

**Table 6.** Evaluation parameters for the best performing classifiers with *αa* as a feature.

