Behavioral Sequence Modeling with Ensemble Learning

TL;DR

This paper introduces an ensemble learning framework using Hidden Markov Models for sequence analysis to improve behavior modeling across various domains, handling fragmented data and imbalanced scenarios effectively.

Contribution

It proposes a novel ensemble-based sequence modeling approach with an interpretable, scalable framework suitable for diverse applications and learning settings.

Findings

Enhanced performance in behavior sequence classification

Robust comparison across sequences of different lengths

Effective handling of imbalanced and scarce data

Abstract

We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior. We discuss framing common problems in fields like healthcare, finance, and e-commerce as sequence modeling tasks, and address challenges related to constructing coherent sequences from fragmented data and disentangling complex behavior patterns. We present a framework for sequence modeling using Ensembles of Hidden Markov Models, which are lightweight, interpretable, and efficient. Our ensemble-based scoring method enables robust comparison across sequences of different lengths and enhances performance in scenarios with imbalanced or scarce data. The framework scales in real-world scenarios, is compatible with downstream feature-based modeling, and is applicable in both supervised and unsupervised learning settings. We demonstrate the effectiveness of our method with results on a longitudinal human behavior dataset.