A guided random forest based feature selection approach for activity recognition

Selection of relevant and non-redundant features for human activity classification is a crucial task in human activity recognition studies, where researchers try to identify the minimum number of features that can still achieve good classification performance. In this paper, we propose a feature selection method based on guided random forest in the context of human activity recognition. In order to select small set of important features using the guided random forest, we first train an ordinary random forests on the dataset for collecting the feature importance scores, and then, inject the collected importance scores to influence the feature selection process in the guided random forest. The proposed guided random forest has a number of key advantages such as trees in the guided random forest are completely independent from one-another, allows parallel computing, low computational cost as it grows only two ensembles, and can select a very small set of high quality features without losing classification accuracy. Using five benchmark datasets, we show that the guided random forest can select compact feature subsets compared to previously proposed methods, while preserving recognition accuracy. We also apply random forests classifier to evaluate the effectiveness of the feature selection methods and demonstrate that random forests with feature subset selected by guided random forest performs comparatively better than feature subsets selected by other feature selection methods.