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Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
Gerasimos Spanakis, Gerhard Weiss, Anne Roefs
1612 - 1613
10.3233/978-1-61499-672-9-1612
Frontiers in Artificial Intelligence and Applications
Volume 285: ECAI 2016
Abstract

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.

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