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Ecological Momentary Assessment (EMA) data is organized in multiple levels(per-subject, per-day, etc.) and this particular structure should be taken intoaccount in machine learning algorithms used in EMA like decision trees and itsvariants. We propose a new algorithm called BBT (standing for Bagged BoostedTrees) that is enhanced by a over/under sampling method and can provide betterestimates for the conditional class probability function. Experimental resultson a real-world dataset show that BBT can benefit EMA data classification andperformance.

Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data