Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
2016
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.
Keywords:
- Correction
- Cite
- Save
- Machine Reading By IdeaReader
9
References
0
Citations
NaN
KQI