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Title: Geographic Boosting Tree: Modeling Non-Stationary Spatial Data
Non-stationarity is often observed in Geographic datasets. One way to explain non-stationarity is to think of it as a hidden "local knowledge" that varies across space. It is inherently difficult to model such data as models built for one region do not necessarily fit another area as the local knowledge could be different. A solution for this problem is to construct multiple local models at various locations, with each local model accounting for a sub-region within which the data remains relatively stationary. However, this approach is sensitive to the size of data, as the local models are only trained from a subset of observations from a particular region. In this paper, we present a novel approach that addresses this problem by aggregating spatially similar sub-regions into relatively large partitions. Our insight is that although local knowledge shifts over space, it is possible for multiple regions to share the same local knowledge. Data from these regions can be aggregated to train a more accurate model. Experiments show that this method can handle non-stationary and outperforms when the dataset is relatively small.  more » « less
Award ID(s):
2018611 1920182 1532061 1338922
NSF-PAR ID:
10275854
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Page Range / eLocation ID:
1205 to 1210
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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