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Pattern recognition with evidential knowledge
Author(s) -
Vila M. A.,
Delgado M.,
GómezSkarmeta A. F.
Publication year - 1999
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199902)14:2<145::aid-int2>3.0.co;2-3
Subject(s) - measure (data warehouse) , set (abstract data type) , dempster–shafer theory , artificial intelligence , mathematics , fuzzy set , probability measure , computer science , evidential reasoning approach , pattern recognition (psychology) , fuzzy logic , machine learning , data mining , statistics , decision support system , business decision mapping , programming language
Many real situations may be seen as classifications problems if the information used to relate items with classes is a probability measure on ( X × C ) (set of all subsets of X × C ). It is well known that a probability measure on ( X × C ) induces two evidence measures on X × C . We will formulate a classification model where it is assumed classes are fuzzy sets and the available information is evidential. Two bad classification loss functions will be established for this purpose, by using Dempster's upper and lower integrals. ©1999 John Wiley & Sons, Inc.