Behavioral Sequence Modeling with Ensemble Learning
This work addresses behavior modeling challenges in fields like healthcare and finance, but it appears incremental as it builds on existing sequence analysis methods.
The paper tackled the problem of modeling human behavior by using sequential context, presenting a framework based on Ensembles of Hidden Markov Models that is lightweight and interpretable, and demonstrated its effectiveness on a longitudinal dataset.
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.