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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.25395 |
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Table of Contents:
- This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional crowdsourcing, which relies on human labels, the framework models the outputs of clustering algorithms as noisy annotations. Experiments on both simulated and real-world datasets show that, although the mixsemble is not always the single top performer, it consistently approaches the best result and avoids poor outcomes. This robustness makes it a practical alternative when the true data structure is unknown, especially for non-expert users.