A non-parametric method to determine basic probability assignment for classification problems

Applied Intelligence

Volumn 41, Issue 3, 2014, Pages 681-693

  Xu, Peida ^a   Su, Xiaoyan ^a   Mahadevan, Sankaran ^b   Li, Chenzhao ^b   Deng, Yong ^c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b VANDERBILT UNIVERSITY   (United States)

^c SOUTHWEST UNIVERSITY   (China)

Author keywords

Basic probability assignment; Belief function; Classification; Dempster Shafer evidence theory; Gaussian process regression; Information fusion

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA FUSION; DECISION MAKING; DECISION THEORY; INFORMATION FUSION; KNOWLEDGE REPRESENTATION; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS;

BASIC PROBABILITY ASSIGNMENT; BELIEF FUNCTION; DECISION MAKING UNDER UNCERTAINTY; DEMPSTER SHAFER EVIDENCE THEORY; ENGINEERING APPLICATIONS; GAUSSIAN PROCESS REGRESSION; PROBABILITY DENSITY FUNCTION (PDF); STATE-OF-THE-ART ALGORITHMS;

PROBABILITY DENSITY FUNCTION;

EID: 84918784860     PISSN: 0924669X     EISSN: 15737497     Source Type: Journal    
DOI: 10.1007/s10489-014-0546-9     Document Type: Article

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