Name
Abstract | ||
|---|---|---|
Predicting the future actions of individuals from geospatial data has the potential to provide a basis for tailored services. This work presents the Predictive Context Tree (PCT), a new hierarchical classifier based on the Context Tree summary model [8]. The PCT is capable of predicting the future contexts and locations of individuals to provide a basis for understanding not only where a user will be, but also what type of activity they will be performing. Through a comparison to established techniques, this paper demonstrates the applicability of the PCT by showing increased accuracies for location prediction, and increased utility through context prediction. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2996913.2996993 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Context Prediction, Geospatial Systems, Hierarchical Classifier, Location Prediction, Trajectories | Geospatial analysis,Data mining,Computer science,Artificial intelligence,Hierarchical classifier,Location prediction,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alasdair Thomason | 1 | 15 | 3.58 |
| Nathan Griffiths | 2 | 115 | 15.49 |
| Victor Sanchez | 3 | 144 | 31.22 |