Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
This addresses classification challenges in EMA data for researchers and practitioners in fields like psychology or healthcare, but it appears incremental as it combines existing techniques.
The authors tackled the problem of classifying Ecological Momentary Assessment (EMA) data, which has a multi-level structure, by proposing a new algorithm called BBT (Bagged Boosted Trees) enhanced with over/under sampling. The result showed that BBT can benefit EMA data classification and performance, though no concrete numbers were provided.
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.