Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
LS Bound based gene selection for DNA microarray data
Bioinformatics
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
2005 Special Issue: Emotion recognition in human-computer interaction
Neural Networks - Special issue: Emotion and brain
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Emotion Classification of Audio Signals Using Ensemble of Support Vector Machines
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
Expert Systems with Applications: An International Journal
Speech emotion classification and public speaking skill assessment
HBU'10 Proceedings of the First international conference on Human behavior understanding
| Hi-index | 0.00 |
One of the primary aims in human-computer interaction research is to develop an ability to recognize affective state of the user. Such ability is indispensable to have a more human-like nature in human-computer interaction. However, the researches in this direction are not mature and intensive efforts have only been witnessed recently. This work envisages the possibility of enhancing feature selection phase of emotion detection task to obtain robust parameters which will be determined from verbal information to achieve an improved affective human-computer interaction. As highly informative feature selection is believed to be a more critical factor than classifier itself, recent studies have increasingly focussed on determining features that contribute more to the classification problem. Two new frameworks for multi-class emotion detection problem are proposed in this paper, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. Evaluation of the selected final features is accomplished by multi-class classifiers. Results show that the proposed frameworks are successful in terms of attaining lower average cross-validation error.