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Sequential Classification Based on Regression
Author(s) -
KrzyŚAko M.
Publication year - 1978
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710200408
Subject(s) - mathematics , value (mathematics) , bayes' theorem , population , division (mathematics) , statistics , set (abstract data type) , object (grammar) , combinatorics , regression , linear regression , pattern recognition (psychology) , artificial intelligence , bayesian probability , computer science , medicine , arithmetic , environmental health , programming language
Let us consider m general populations π 1 , …,π m . Each object belonging to these populations is represented by ( p ± 1) characteristics x 1 , x 2 ,…,x p ,y. A certain object, which is an element of one of the m general populations π 1 ,…,π m has to be classified into the correct population. It will be assumed that knowledge of the value of the characteristic y would permit its correct classification, but that the observation of this characteristic is expensive, difficult or dangerous, as e.g. in medical applications. y is correlated with a set of p characteristics x 1 ,x 2 ,…,x p , which are observed sequentially. The classification procedure is based on the division of the space of the observed value of characteristics x 1 ,x 2 ,…,x p into nonintersecting areas determined so as to minimize the value of BAYES' risk given by equation (3).