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Ensemble Methods for Spatial Data Stream Classification.
- Source :
- Procedia Computer Science; 2023, Vol. 224, p155-162, 8p
- Publication Year :
- 2023
-
Abstract
- In this paper, we study the problem of classification of data in a spatial data stream. The classification of spatial objects in a static data set has been well studied. However, classifying objects in a spatial data stream has received very little attention. We propose an iterative ensemble approach for deep learning of a spatial data stream. Using several deep neural networks, our strategy iteratively performs training and testing of the classifier, with the goal of reaching a desired accuracy, and the same accuracy that would be achieved as a classifier that is trained and tested with the entire dataset. An experimental evaluation and comparison of the ensemble approach shows improvement over a previously proposed iterative deep learning strategy. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
DEEP learning
CLASSIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 224
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 172888238
- Full Text :
- https://doi.org/10.1016/j.procs.2023.09.023