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.