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Improved SMOTE Algorithm to Deal with Imbalanced Activity Classes in Smart Homes

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Abstract

Performance of resident-activity-recognition systems is an important measure in the evaluation of smart homes performance. An imbalanced distribution of activity classes, however, severely degrades this performance. Traditional approaches towards realization of activity recognition focus on the improvement of recognition algorithms rather than imbalanced-data adjusting. Even state-of-the-art recognition algorithms have been limited to exclusively improving activity-recognition performance. The proposed study focuses on imbalanced-data adjusting and presents an improved Synthetic Minority Oversampling Technique (SMOTE) algorithm to address issues concerning imbalanced activity classes. Instead of linear interpolation, the proposed algorithm uses the Euclidean distance of each minor activity to adjust the distribution of activity classes, thereby generating new synthetic minority activities in the neighborhood of remaining minority-class examples. Two public datasets were utilized in this study to validate the improved SMOTE algorithm. Results demonstrate that the proposed approach favorably outperforms traditional SMOTE algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61672122, 61602077, 51679105, 51409117); the Fundamental Research Funds for the Central Universities (Nos. 3132016348, 3132018194); ANHUI Province Key Laboratory of Affective Computing & Advanced Intelligent Machine (No. ACAIM20180001); and the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province (No. 2018RYJ09).

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Shikai Guo, Yaqing Liu, Rong Chen & Xiangxin Wang

  2. ANHUI Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, 230009, China

    Yaqing Liu & Xiao Sun

  3. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, 643000, China

    Yaqing Liu

  4. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yaqing Liu

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  1. Shikai Guo
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  4. Xiao Sun
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  5. Xiangxin Wang
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All authors contributed equally to this study and have read and approved the final draft of this manuscript.

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Correspondence to Yaqing Liu.

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Guo, S., Liu, Y., Chen, R. et al. Improved SMOTE Algorithm to Deal with Imbalanced Activity Classes in Smart Homes. Neural Process Lett 50, 1503–1526 (2019). https://doi.org/10.1007/s11063-018-9940-3

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