A statistical approach to combining multisource information in one-class classifiers

Simonson, Katherine M.; Derek West, R.; Hansen, Ross L.; LaBruyere, Thomas E.; Van Benthem, Mark H.

doi:10.1002/sam.11342

A statistical approach to combining multisource information in one-class classifiers

Journal Article · Thu Jun 08 04:00:00 EDT 2017 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11342· OSTI ID:1399493

^[1]; Derek West, R. ^[1]; Hansen, Ross L. ^[1]; LaBruyere, Thomas E. ^[1]; Van Benthem, Mark H. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

A new method is introduced in this paper 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. Finally, the method is seen to be particularly effective for relatively small training samples.

View Accepted Manuscript (DOE)

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, Journal Name: Statistical Analysis and Data Mining Journal Issue: 4 Vol. 10; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers Simonson, Katherine; West, R. Derek; Hansen, Ross https://doi.org/10.6084/m9.figshare.4620124 The current record is supplemented by this dataset	dataset	January 2017
Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers Simonson, Katherine; West, R. Derek; Hansen, Ross https://doi.org/10.6084/m9.figshare.4620124.v1 The current record is supplemented by this dataset	dataset	January 2017
Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers [Supplemental Data] Simonson, Katherine; West, R. Derek; Hansen, Ross https://doi.org/10.6084/m9.figshare.c.3682954 The current record is supplemented by this dataset	dataset	June 2017
Dataset for: A Statistical Approach to Combining Multi-Source Information in One-Class Classifiers Simonson, Katherine; Hansen, Ross; LaBruyere, Thomas https://doi.org/10.6084/m9.figshare.c.3682954.v1 The current record is supplemented by this collection	collection	June 2017

A statistical approach to combining multisource information in one-class classifiers

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