Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

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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.
Original languageEnglish
Title of host publicationECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016)
Pages1612-1613
Number of pages2
DOIs
Publication statusPublished - 2016

Cite this

Spanakis, G., Weiss, G., & Roefs, A. (2016). Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data. In ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016) (pp. 1612-1613) https://doi.org/10.3233/978-1-61499-672-9-1612