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Human activity recognition using fuzzy proximal support vector machine for multicategory classification

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Abstract

Applying machine learning tools to human activity analysis presents two major challenges: Firstly, the transformation of actions into multiple attributes increases training and testing time significantly. Secondly, the presence of noises and outliers in the dataset adds complexity and makes it difficult to implement the activity detection system efficiently. This paper addresses both of the challenges by proposing a kernel fuzzy proximal support vector machine as a robust classifier for multicategory classification problems. It transforms the input patterns into a higher-dimensional space and assigns each pattern an appropriate membership degree to reduce the effect of noises and outliers. The proposed method only requires the solution of a set of linear equations to obtain the classifiers; thus, it is computationally efficient. The computer simulation results on ten UCI benchmark problems show that the proposed method outperforms established methods in predictive accuracy. Finally, numerical results from three human activity recognition problems validate the applicability of the proposed method.

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Notes

  1. Similar type of problems is also present in a variety of other classification tasks in the real world, including credit risk evaluation [57], image retrieval [59], and human identification [34].

  2. Such a map exists iff \(\forall ~h: {\mathbb {R}}^n\longrightarrow {\mathbb {R}}, \int h^2 (x)dx < \infty \), we have \(\displaystyle \int K(p,q) h(p) h(q) dp dq \ge 0\) (see [39]).

  3. There is no global rule to choose the most appropriate kernel function and its associated parameters. However, the performance of SVM depends on the choice of the kernel function and its parameters.

  4. There is no missing value in any dataset.

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Acknowledgements

The authors sincerely thank the reviewers for the recommendation, valuable comments and the interesting suggestions which have considerably improved the presentation of the paper. The first author is also grateful to the Ministry of Human Resource Development, India, for financial support, to carry out this work.

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  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247 667, India

    Scindhiya Laxmi & S. K. Gupta

  2. Indian Institute of Management Udaipur, Udaipur, 313 001, India

    Sumit Kumar

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Laxmi, S., Kumar, S. & Gupta, S.K. Human activity recognition using fuzzy proximal support vector machine for multicategory classification. Knowl Inf Syst 65, 4585–4611 (2023). https://doi.org/10.1007/s10115-023-01911-9

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