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Efficient Observation Time Window Segmentation for Administrative Data Machine Learning
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DOI：10.1109/ACCESS.2024.3484270.png)
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Abstract
En 中文
Machine learning models benefit when allowed to learn from temporal trends in time-stamped administrative data. These trends can be represented by dividing a model's observation window into time segments or bins. Model training time and performance can be improved by representing each feature with a different time resolution. However, this causes the time bin size hyperparameter search space to grow exponentially with the number of features. This paper proposes a computationally efficient time series analysis to investigate binning (TAIB) technique that determines which subset of data features benefit the most from time bin size hyperparameter tuning. This technique is demonstrated using hospital and housing/homelessness administrative data sets. The results show that TAIB leads to models that are not only more efficient to train but can perform better than models that default to representing all features with the same time bin size.
Keywords:
Machine learning
Data models
Market research
Vectors
Vehicle dynamics
Tuning
Time series analysis
Recurrent neural networks
Physiology
Null value
Administrative data
time window segmentation
machine learning
hospital
homelessness
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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