Comparative Study of Classifiers on Human Activity Recognition by Different Feature Engineering Techniques

Evaluates K-Nearest Neighbors, Decision Tree, Random Forest, Gaussian Naive Bayes, and an MLP classifier on a UCI human-activity-recognition dataset, comparing accuracy across feature sets produced by different feature-selection and PCA-based dimensionality-reduction techniques. Finds that a back-propagation MLP achieves the best accuracy across the tested feature sets.

Recommended citation: Tasmin, M., Ishtiak, T., Ruman, S.U., Suhan, A.R.C., Islam, N.M.S., Jahan, S., Ahmed, S., Zulminan, M.S., Saleheen, A.R., Rahman, R.M. Comparative Study of Classifiers on Human Activity Recognition by Different Feature Engineering Techniques. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 93-101.
Download Paper