Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

Gerasimos Spanakis; Gerhard Weiss; Anne Roefs

doi:10.3233/978-1-61499-672-9-1612

Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

Gerasimos Spanakis^*
, Gerhard Weiss
, Anne Roefs

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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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 language	English
Title of host publication	Frontiers in Artificial Intelligence and Applications
Editors	Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hullermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
Pages	1612-1613
Number of pages	2
ISBN (Electronic)	9781614996712
DOIs	https://doi.org/10.3233/978-1-61499-672-9-1612
Publication status	Published - 2016

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title = "Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data",

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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Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data. / Spanakis, Gerasimos ; Weiss, Gerhard ; Roefs, Anne.
Frontiers in Artificial Intelligence and Applications. ed. / Gal A. Kaminka; Maria Fox; Paolo Bouquet; Eyke Hullermeier; Virginia Dignum; Frank Dignum; Frank van Harmelen. 2016. p. 1612-1613.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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AU - Weiss, Gerhard

AU - Roefs, Anne

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N2 - 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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DO - 10.3233/978-1-61499-672-9-1612

M3 - Conference article in proceeding

SN - 9781614996729

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BT - Frontiers in Artificial Intelligence and Applications

A2 - Kaminka, Gal A.

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Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

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