Genetic Ensemble Biased ARTMAP Method of ECG-Based Emotion Classification
This study is an attempt to design a method for an autonomous pattern classification and recognition system for emotion recognition. The proposed system utilizes Biased ARTMAP for pattern learning and classification. The ARTMAP system is dependent on training sequence presentation to determine the effectiveness of the learning processes, as well as the strength of the biasing parameter, lambda λ. The optimal combination of λ and training sequence can be computed efficiently using a genetic permutation algorithm. The best combinations were selected to train individual ARTMAPs as voting members, and the final class predictions were determined using probabilistic ensemble voting strategy. Classification performance can be improved by implementing a reliability threshold for training data. Reliability metric for each training sample was computed from the current voter output, and unreliable training samples were excluded from the performance calculation. Individual emotional states are highly variable and are subject to evolution from personal experiences. For this reason, the above system is designed to be able to perform learning and classification in real-time to account for inter-individual and intra-individual emotional drift over time.
KeywordsLinear Discriminant Analysis Emotion Recognition Training Sequence Adaptive Resonance Theory Plurality Vote
Unable to display preview. Download preview PDF.
- 1.De Silva, L.C., Miyasato, T., Nakatsu, R.: Facial emotion recognition using multimodal information. In: Proceedings on International Conference on Information, Communications, and Signal Processing, vol. 1, pp. 397–401 (1997)Google Scholar
- 2.Busso, C., Deng, Z., Yildrim, S., Bulut, M., Lee, C.M., Kazemzadeh, A., Lee, S., Neumann, U., Narayanan, S.: Analysis of emotion recognition using facial expressions and multimodal information. In: Proceedings of the 6th International Conference on Multimodal Interfaces, pp. 205–211 (2004)Google Scholar
- 3.Wagner, J., Kim, J., Andre, E.: From physiological signals to emotion: Implementing and comparing selected methods for feature extraction and classification. In: IEEE International Conference on Multimedia and Expo, pp. 940–943 (2005)Google Scholar
- 7.Choi, J., Gutierrez-Osuna, R.: Using heart rate monitors to detect mental stress. In: 6th International Workshop on Wearable and Implantable Body Sensor Networks, pp. 219–223 (2009)Google Scholar
- 8.Plutchik, R.: The nature of emotions. American Scientist (2001)Google Scholar
- 14.Wagner, J.: The Augsburg Biosignal Toolbox (2009), http://www.informatik.uni-augsburg.de/en/chairs/hcm/projects/aubt/ (retrieved June 29, 2011
- 15.Frank, A., Suncion, A.: UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science (2010), http://archive.ics.uci.edu/ml (retrieved November 2011)