Abstract
Decoding the emotional state of a person has a variety of applications. It could be used in human-computer interaction (HCI) or like follow-ups in the therapeutic techniques. Recently, emotion recognition is one of topic that researchers are most interested in and until now, there are several studies relating to the emotion using devices and techniques. To recognize human emotions, various physiological signals have been widely used. In this research, we propose a novel approach for the emotion classification using several physiological signals to classify eight emotions according to the Clynes sentograph protocol of Manfred Clynes. The study has two main objectives. On the one hand a comparative study to choose the best classifiers that addresses the emotion classification problem. And On the other hand to develop an ensemble classifiers approach.
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References
Schacter, D.: Psychology, 2nd edn, p. 310. Worth Publishers, New York (2011). ISBN
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)
Izard, C.: Human Emotions. Plenum Press, New York (1977/2001)
Ortony, A., Turner, T.J.: What’s basic about basic emotions? Psychol. Rev. 97, 315–331 (1990)
Darwin, C., Ekman, P., Prodger, P.: The Expression of the Emotions in Man and Animals. Oxford University Press, Oxford (1998)
Cannon, W.B.: The james-lange theory of emotions: A critical examination and an alternative theory. Am. J. Psychol. 39, 106–124 (1927)
Damasio, A., Sutherland, S.: Descartes’ error: Emotion, reason and the human brain. Nature 372, 287–287 (1994)
Plutchik, R.: The Emotions. University Press of America, New York (1991)
Lisa, F.: Valence focus and arousal focus: Individual differences in the structure of affective experience. J. Pers. Soc. Psychol. 69(1), 153–166 (1995)
Clynes, M.: Sentography: Dynamic forms of communication of emotion and qualities. Comput. Biol. Med. 3, 119–130 (1972)
Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)
Sheng, H.K., Mohamed, A.: 3D HMM-based facial expression recognition using histogram of oriented optical flow (2015)
Hongying, M., Nadia, B., Yangdong, D., Jinkuang, C.: Time-delay neural network for continuous emotional dimension prediction from (2015)
Muthusamy, H., Polat, K., Yaacob, S.: Improved emotion recognition using gaussian mixture model and extreme learning machine in speech and glottal signals. Math. Probl. Eng. 2015, 13 (2015). doi:10.1155/2015/394083. Article ID 394083
Bhaskar, J., Sruthi, K., Nedungadi, P.: Hybrid approach for emotion classification of audio conversation based on text and speech mining. Procedia Comput. Sci. 46, 635–643 (2015)
Park, B.J., Jang, E.H., Kim, S.H., Huh, C., Chung, M.A.: The design of fuzzy C-means clustering based neural networks for emotion classification. In IFSA World Congress and NAFIPS Annual Meeting, pp. 413–417 (2013)
Zheng, L., Zhu, Y., Peng, Y., Lu, L.: EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia and Expo (2014)
Verma, G.K., Tiwary, U.S.: Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage 102, 162–172 (2014)
Lokannavar, S., Lahane, P., Gangurde, A., Chidre, P.: Emotion recognition using EEG signals. Emotion 4(5) (2015)
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1), 1–39 (2010)
Dietterich, T.G.: Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems, pp. 1–15 (2000)
Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)
Vaish, A., Kumari, P.: A comparative study on machine learning algorithms in emotion state recognition using ECG. In: Proceedings of the Second International Conference on Soft Computing for Problem Solving (2014)
Mano, L.Y., Giancristofaro, G.T., Faical, B.S., Libralon, G.L., Pessin, G., Gomes, P.H., Ueyama, J.: Exploiting the use of ensemble classifiers to enhance the precision of user’s emotion classification. In: International Conference on Engineering Applications Of Neural Networks (INNS) (2015)
Sun, Y., Wen, G.: Ensemble softmax regression model for speech emotion recognition. Multimed.Tools Appl. 76(6), 1–24 (2016)
Perikos, I., Hatzilygeroudis, I.: Recognizing emotions in text using ensemble of classifiers. Eng. Appl. Artif. Intell. 51, 191–201 (2016)
Rani, P.I., Muneeswaran, K.: Recognize the facial emotion in video sequences using eye and mouth temporal gabor features. Multim. Tools Appl. 76(7), 1–24 (2016)
Jain, S., Durgesh, M., Ramesh, T.: Facial expression recognition using variants of LBP and classifier fusion. In: Proceedings of International Conference on ICT for Sustainable Development, pp. 725–732 (2016)
Neoh, S.C., Mistry, K., Zhang, L., Lim, C.P., Fielding, B.: A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition (2016)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: National Conference on Artificial Intelligence, pp. 223–228 (1992)
Cao, Y., Wu, J.: Projective ART for clustering data sets in high dimensional spaces. Neural Netw. 15(1), 105–120 (2002)
Haijian, S.: Best-first decision tree learning. Hamilton, NZ (2007)
Gaines, B.R., Compton, J.P.: Induction of ripple-down rules applied to modeling large databases. Intell. Inf. Syst. 5(3), 211–228 (1995)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59, 161 (2005)
Cleary, J.G., Trigg, L.E.: K*: An instance-based learner using an entropic distance measure. In: 12th International Conference on Machine Learning (1995)
Healey, J., Picard, R.W.: Eight-emotion sentics data (2002). http://affect.media.mit.edu. Retrieved 12 Aug 2016
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Chaibi, M.W. (2018). An Ensemble Classifiers Approach for Emotion Classification. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_11
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DOI: https://doi.org/10.1007/978-3-319-59480-4_11
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