Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

TL;DR

This paper introduces BBT, a novel ensemble algorithm combining bagging and boosting, tailored for classifying multi-level EMA data, improving probability estimates and overall performance.

Contribution

The paper presents BBT, an innovative ensemble method that incorporates over/under sampling, specifically designed for EMA data classification, addressing its hierarchical structure.

Findings

BBT improves classification accuracy on EMA data

Enhanced probability estimation with BBT

Beneficial performance gains demonstrated on real-world datasets

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.