用集成隐马尔可夫模型分析行为序列，提升复杂行为模式的建模效果。
Behavioral Sequence Modeling with Ensemble Learning
- 构建集成隐马尔可夫模型框架，利用序列上下文捕捉行为动态
- 在长序列与小样本场景下表现稳定，支持不同长度序列的可靠比较
- 轻量可解释，适用于医疗、金融等多领域行为分析
我们研究了序列分析在行为建模中的应用，指出序列上下文常比聚合特征更能揭示人类行为本质。将医疗、金融、电商等领域常见问题转化为序列建模任务，解决碎片化数据构建连贯序列及复杂行为模式解耦的挑战。提出基于隐马尔可夫模型集成的序列建模框架，具有轻量、可解释、高效的特点。其集成评分方法能稳健比较不同长度序列，在数据不平衡或稀缺场景下仍具优势。该框架可扩展至真实世界应用，兼容下游特征建模，适用于监督与非监督学习。在纵向人类行为数据集上的实验验证了方法的有效性。
原文摘要 · Abstract (English)
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.
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