A comprehensive nonlinear processing method using Kernel Principal Component Analysis and Gradient Boosting Decision Tree achieved a classification accuracy of 93.42% for four emotions.
A comprehensive nonlinear processing method using KPCA and GBDT achieved high accuracy (93.42%) in classifying emotions from multichannel physiological signals.
Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.
Zhang et al. (Sun,) conducted a other in Emotion recognition (n=29). Comprehensive nonlinear processing method (KPCA and GBDT) vs. Linear or partial nonlinear processing was evaluated on Classification accuracy of four emotions. A comprehensive nonlinear processing method using Kernel Principal Component Analysis and Gradient Boosting Decision Tree achieved a classification accuracy of 93.42% for four emotions.
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