| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 102 === In this thesis, we study different classification strategies for emotion recognition based on electrocardiogram (ECG) to recognize five kinds of emotions, including neutral (non-stimulated state), happy, stress, sad, and anger. The participants consisted of 10 male and 10 female students who watched video programs of two to four minutes in length to stimulate distinct emotions.
Multiclass classification using binary classifiers need to combine several of binary classifiers. How to extend it effectively is still an ongoing research topic. In regular settings, one feature set is selected for all the binary classifiers to. In this study, we hypothesize that using unique feature set for the classifier can raise the accuracy for multiclass classification. Experiment were designed to the performance of combinations of three combining architectures, two feature selectors, and two kinds of classifications. The three architecture includes one-against-one, directed acyclic graph, and tree, which we propose for emotion recognition in this research. The two feature selectors are sequential forward selection and genetic algorithms. The two kinds of classifiers includes the support vector machine which is binary and the k-nearest neighborhood which is not binary classifier.
Comparing the performance of the above-mentioned different combinations, we found the combination of the tree architecture and the SVM classifier could reduce the number of classifiers yet retain acceptable accuracy. Combination of GA selector and OVO architecture using k-NN classifier with multiple feature sets can achieve the highest accuracy of 94%.
|
|---|
