title: Discriminant analysis algorithm based on a distance function and on a Bayesian decision.
abstract: We propose a new algorithm for the allocation of an individual to one of several possible groups or populations. The algorithm enables us to define a finite partition over the sample space, based on distance function. This partition is used, jointly with the application of a standard Bayesian decision rule, to allocate individuals to the populations. The algorithm also provides a measure of the allocation confidence for each individual, in a similar manner to that of logistic regression. The error rates for classification are also computed using the leave-one-out method. Results are compared with those obtained with other discriminant analysis techniques previously reported: Fisher's linear discriminant function, the quadratic discriminant function, logistic discrimination, and others.
authors.affiliation: Departament d'Estadística, Universitat de Barcelona, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Villarroya
authors.forename: M
authors.initials: M
authors.lastname: Ríos
authors.forename: J M
authors.initials: JM
authors.lastname: Oller
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D001786:Blood Glucose / Q000032:analysis; D002415:Cats; D002648:Child; D003128:Coma / Q000401:mortality / Q000503:physiopathology; D016883:Diabetic Ketoacidosis / Q000401:mortality* / Q000503:physiopathology; D016002:Discriminant Analysis; D006801:Humans; D008433:Mathematics; D011336:Probability; D012008:Recurrence; D015996:Survival Rate
chemical_list: D001786:Blood Glucose