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A novel feature selection method based on comparison of correlations for human activity recognition problems

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Abstract

In human activity recognition studies it is important to identify an optimal set with the minimum number of features that will potentially improve the recognition rate. In the current paper we introduce a promising feature selection method that exploits the differences on the correlation structure of the features, between the different classes of the target variable. Using the recordings of triaxial accelerometers and gyroscopes, we extracted several features and created subsets according to the activities performed. For each subset, we calculated the pairwise correlation coefficients of the features and compared the feature correlations of different subsets. By identifying the significantly different correlations we ranked the variables participating in those correlations based on their frequency of appearance and thus created a subset of features that will optimize the performance of a classification algorithm. The method allows the researcher to select the desired number of features to be included in the classification. Two publicly available datasets were used to evaluate the performance of the proposed methodology in binary and multiclass classification problems. The evaluation revealed quite promising results of the methodology that was compared to the performance of the whole feature set and of a feature selection method that has been extensively used in activity recognition studies.

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Acknowledgements

This research has been cofinanced by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code:T1EDK-00686)

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Correspondence to Athina Tsanousa.

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Tsanousa, A., Meditskos, G., Vrochidis, S. et al. A novel feature selection method based on comparison of correlations for human activity recognition problems. J Ambient Intell Human Comput 11, 5961–5975 (2020). https://doi.org/10.1007/s12652-020-01836-z

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