A statistical approach to combining multisource information in one-class classifiers
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
A new method is introduced for combining information from multiple sources to support one‐class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher's technique for combining p ‐values, modified to handle nonindependent sources. Classifier outputs take the form of fused p ‐values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorous assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high‐consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. The method is seen to be particularly effective for relatively small training samples.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); SNL Laboratory Directed Research and Development (LDRD) program
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1399493
- Alternate ID(s):
- OSTI ID: 1400859
- Report Number(s):
- SAND2017-2026J; 651299
- Journal Information:
- Statistical Analysis and Data Mining, Vol. 10, Issue 4; ISSN 1932-1864
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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