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.
Co-Author: Scindhiya Laxmi, S. K. Gupta
Journal: Knowledge and Information Systems
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