Integration of multiple data sources to provide better information
Fusion of the data from two sources (dimensions #1 & #2) can yield a classifier superior to any classifiers based on dimension #1 or dimension #2 alone.
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.
Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place.[1] Low-level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs.
For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion.
The concept of data fusion has origins in the evolved capacity of humans and animals to incorporate information from multiple senses to improve their ability to survive. For example, a combination of sight, touch, smell, and taste may indicate whether a substance is edible.[2]
^Klein, Lawrence A. (2004). Sensor and data fusion: A tool for information assessment and decision making. SPIE Press. p. 51. ISBN 978-0-8194-5435-5.
^Hall, David L.; Llinas, James (1997). "An introduction to multisensor data fusion". Proceedings of the IEEE. 85 (1): 6–23. doi:10.1109/5.554205. ISSN 0018-9219.
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