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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02174 |
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| _version_ | 1866917828666327040 |
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| author | Kawawa-Beaudan, Maxime Sood, Srijan Palande, Soham Mani, Ganapathy Balch, Tucker Veloso, Manuela |
| author_facet | Kawawa-Beaudan, Maxime Sood, Srijan Palande, Soham Mani, Ganapathy Balch, Tucker Veloso, Manuela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Behavioral Sequence Modeling with Ensemble Learning Kawawa-Beaudan, Maxime Sood, Srijan Palande, Soham Mani, Ganapathy Balch, Tucker Veloso, Manuela Machine Learning Artificial Intelligence 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. |
| title | Behavioral Sequence Modeling with Ensemble Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.02174 |