Abstract
Human-computer intelligent interaction (HCII) is becoming more and more important in daily life, and emotion recognition is one of the important issues of HCII. In this paper, a novel emotion recognition method based on dynamic ensemble feature selection is proposed. Firstly, a feature selection algorithm is proposed based on rough set and domain-oriented data-driven data mining theory, which can get multiple reducts and candidate classifiers accordingly. Secondly, the nearest neighborhood of each unseen sample is found in a validation subset and the most accuracy classifier is selected from the candidate classifiers. In the end, the dynamically selected classifier is used to recognize each unseen sample. The proposed method is proved to be an effective and suitable method for emotion recognition according to the result of comparative experiments.
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References
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Ditterrich, T.G.: Machine learning research: four current direction. Artificial Intelligence Magzine 4, 97–136 (1997)
Freund, Y.: Boosting a weak algorithm by majority. Information and Computation 121(2), 256–285 (1995)
Ohsuga, S.: Knowledge discovery as translation. In: Lin, T.Y., et al. (eds.) Foundations of Data Mining and Knowledge Discovery, pp. 1–19. Springer, Heidelberg (2005)
Picard, R.W.: Affective computing: Challenges. International Journal of Human-Computer Studies 59(1), 55–64 (2003)
Russell, B., Christian, P.: The role of affect and emotion in HCI. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 1–11. Springer, Heidelberg (2008)
Scott, B., Clifford, N.: Emotion in hunam-computer interaction. In: Julie, A.J., Sears, A. (eds.) The Human-computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pp. 81–93. Lawrence Erlbaum Associates Press, Mahwah (2003)
Wang, G.Y.: Introduction to 3DM: Domain-oriented data-driven data mining. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS, vol. 5009, pp. 25–26. Springer, Heidelberg (2008)
Wang, G.Y., Wang, Y.: Domain-oriented data-driven data mining: a new understanding for data mining. Journal of Chongqing University of Posts and Telecommunications 20(3), 266–271 (2008)
Yang, Y., Wang, G.Y., Chen, P.J., et al.: Feature selection in audiovisual emotion recognition based on rough set theory. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 283–294. Springer, Heidelberg (2007)
Zhou, Z.H.: Ensemble learning. In: Li, S.Z. (ed.) Encyclopedia of Biometrics, pp. 1–5. Springer, Heidelberg (2009)
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Yang, Y., Wang, G., Kong, H. (2009). Emotion Recognition Based on Dynamic Ensemble Feature Selection. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_22
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DOI: https://doi.org/10.1007/978-3-642-00563-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00562-6
Online ISBN: 978-3-642-00563-3
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