Summary of Efficient Observation Time Window Segmentation For Administrative Data Machine Learning, by Musa Taib et al.
Efficient Observation Time Window Segmentation for Administrative Data Machine Learning
by Musa Taib, Geoffrey G. Messier
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 Machine learning models benefit when allowed to learn from temporal trends in time-stamped administrative data. By dividing a model’s observation window into time segments or bins, training time and performance can be improved by representing each feature with a different time resolution. However, this approach leads to an exponentially growing search space for the time bin size hyperparameter. To address this challenge, the authors propose a computationally efficient technique called TAIB (Time Series Analysis for Investigating Binning) that determines which subset of features benefit most from time bin size tuning. The technique is demonstrated using hospital and housing/homelessness administrative data sets, showing that TAIB leads to more efficient training and better performance than defaulting to uniform time bins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can get smarter when they learn from patterns in time-stamped data. One way to do this is by breaking down the data into smaller chunks called time segments or bins. This helps the model learn faster and perform better, but it makes it harder to choose the right size for each bin. To solve this problem, researchers developed a new technique called TAIB that quickly figures out which features benefit most from different bin sizes. They tested TAIB using data from hospitals and homelessness, showing that it leads to more efficient training and better results. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning * Time series
