Objective: Multiclass classification with supervised learning, small dataset (25h)
Context: My dataset is composed of mobile network data collected with a smartphone. The labels correspond to the activity of the user (Stationary, Walk, Subway, Train, Car). My features are calculated based on 3 fields: timestamp, ID, and signal strength (SS). All have different overlapping size windows: 15s, 30s, 602, 90s, 120s. So, I have 3 features based on ID and 16 statistical features based on SS for each window size with a total of 95 features.
My Question: Which feature selection should I use? Am I correct saying the features are not independent?
(I'm using python).