Enhancing Classification of Ecological Momentary Assessment Data Using Bagging and Boosting

Gerasimos Spanakis*, Gerhard Weiss, Anne Roefs

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Ecological Momentary Assessment (EMA) techniques gain more ground in studies and data collection among different disciplines. Decision tree algorithms and their ensemble variants are widely used for classifying this type of data, since they are easy to use and provide satisfactory results. However, most of these algorithms do not take into account the multiple levels (per-subject, per-day, etc.) in which EMA data are organized. In this paper we explore how the EMA data organization can be taken into account when dealing with decision trees and specifically how a combination of bagging and boosting can be utilized in a classification task. A new algorithm called BBT (standing for Bagged Boosted Trees) is proposed which is enhanced by an over/under sampling method leading to better estimates of the conditional class probability function. BBT's necessity and effects are demonstrated using both simulated datasets and real-world EMA data collected using a mobile application following the eating behavior of 100 people. Experimental analysis shows that BBT leads to clear improvements with respect to prediction error reduction and conditional class probability estimation.
Original languageEnglish
Title of host publicationIEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI 2016)
PublisherIEEE
Pages388-395
Number of pages8
ISBN (Print)9781509044597
DOIs
Publication statusPublished - 6 Nov 2016
Event28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) - CA, San Jose, United States
Duration: 6 Nov 20168 Nov 2016

Publication series

SeriesInternational Conference on Tools With Artificial Intelligence Proceedings
ISSN1082-3409

Conference

Conference28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
Abbreviated titleICTAI
Country/TerritoryUnited States
CitySan Jose
Period6/11/168/11/16

Keywords

  • Ecological Momentary Assessment
  • Classification Trees
  • Bagging
  • Boosting
  • REGRESSION TREES
  • PREDICTION
  • ALGORITHMS

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