Summary of Ranking and Combining Latent Structured Predictive Scores Without Labeled Data, by Shiva Afshar et al.
Ranking and Combining Latent Structured Predictive Scores without Labeled Data
by Shiva Afshar, Yinghan Chen, Shizhong Han, Ying Lin
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel structured unsupervised ensemble learning model (SUEL) for combining multiple predictors obtained from distributed data sources. The SUEL model exploits the dependency between predictors with continuous predictive scores, ranking them without labeled data and combining them to an ensembled score with weights. To estimate the SUEL model, two correlation-based decomposition algorithms are introduced: constrained quadratic optimization (CQO) and matrix-factorization-based (MF) approaches. The efficacy of these methods is assessed through simulation studies and a real-world application in risk genes discovery. The results demonstrate that the proposed methods can efficiently integrate dependent predictors to an ensemble model without requiring ground truth data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to combine different pieces of information from various sources to make better predictions. This is useful for many problems, like predicting whether someone will develop a certain disease or what the weather will be like tomorrow. The challenge is that each piece of information might not be accurate, and it’s hard to know which ones are most reliable. The paper proposes a new way to combine these pieces of information called SUEL (Structured Unsupervised Ensemble Learning). It uses special algorithms to figure out how much weight to give each piece of information based on its reliability. The results show that this method can be very effective in making good predictions. |
Keywords
» Artificial intelligence » Ensemble model » Optimization » Unsupervised
