Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants. We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function. Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance.
@article{arxiv.1607.01582,
title = {Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data},
author = {Gerasimos Spanakis and Gerhard Weiss and Anne Roefs},
journal= {arXiv preprint arXiv:1607.01582},
year = {2016}
}