A probability model for errors of classification. i. general considerations.

TLDR: This paper seeks to meet the need for a general treatment of the problem of error in classification within an m-attribute classificatory system by finding particular systemsO =f(T, E) which are solvable forT andE givenO.

Abstract: This paper seeks to meet the need for a general treatment of the problem of error in classification. Within an m-attribute classificatory system, an object's typical subclass is that subclass to which it is most often allocated under repeated experimentally independent applications of the classificatory criteria. In these terms, an error of classification is an atypical subclass allocation. This leads to definition of probabilitiesO of occasional subclass membership, probabilitiesT of typical subclass membership, and probabilitiesE of error or, more generally, occasional subclass membership conditional upon typical subclass membership. In the relationshipf: (O, T, E) the relative incidence of independentO, T, andE values is such that generally one can specifyO values givenT andE, but one cannot generally specifyT andE values givenO. Under the restrictions of homogeneity ofE values for all members of a given typical subclass, mutual stochastic independence of errors of classification, and suitable conditions of replication, one can find particular systemsO =f(T, E) which are solvable forT andE givenO. A minimum of three replications of occasional classification is necessary for a solution of systems for marginal attributes, and a minimum of two replications is needed with any cross-classification. Although for such systems one can always specifyT andE values givenO values, the solution is unique for dichotomous systems only.