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Error-correcting output codes for multi-label emotion classification

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Abstract

Multi-modal affective data such as EEG and physiological signals is increasingly utilized to analyze of human emotional states. Due to the noise existed in collected affective data, however, the performance of emotion recognition is still not satisfied. In fact, the issue of emotion recognition can be regarded as channel coding, which focuses on reliable communication through noise channels. Using affective data and its label, the redundant codeword would be generated to correct signals noise and recover emotional label information. Therefore, we utilize multi-label output codes method to improve accuracy and robustness of multi-dimensional emotion recognition by training a redundant codeword model, which is the idea of error-correcting output codes. The experiment results on DEAP dataset show that the multi-label output codes method outperforms other traditional machine learning or pattern recognition methods for the prediction of emotional multi-labels.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (no. 61304262).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Chao Li

  2. School of Computer Software, Tianjin University, Tianjin, China

    Zhiyong Feng & Chao Xu

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  1. Chao Li
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  2. Zhiyong Feng
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  3. Chao Xu
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Correspondence to Chao Xu.

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Li, C., Feng, Z. & Xu, C. Error-correcting output codes for multi-label emotion classification. Multimed Tools Appl 75, 14399–14416 (2016). https://doi.org/10.1007/s11042-016-3608-7

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  • DOI: https://doi.org/10.1007/s11042-016-3608-7

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