Abstract
Real-time data with redundant and irrelevant features can degrade the performance of the classifier. Dataset with more number of features also increases the noise of the data and increases the time complexity of the learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the dimensions of the data. In existing feature selection methods, the resultant feature sets can lead to local optima in the space of feature subsets. In this paper, ensemble-based feature selection approach is proposed to reduce size of the dataset and to improve classification accuracy. Results show that the proposed ensemble approach enhances the classifier performance, with reduced number of features.
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Gopalakrishnan, N., Krishnan, V., Gopalakrishnan, V. (2020). Ensemble Feature Selection to Improve Classification Accuracy in Human Activity Recognition. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore. https://doi.org/10.1007/978-981-15-0146-3_51
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DOI: https://doi.org/10.1007/978-981-15-0146-3_51
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