Class membership probabilities
Encyclopedia
In general proplems of classification
, class membership probabilities reflect the uncertainty with which a given indivual item can be assigned to any given class. Although statistical classification methods by definition generate such probabilities, applications of classification in machine learning
usually supply membership values that do not induce any probabilistic confidence. It is desirable, to transform or re-scale membership values to class membership probabilities, since they are comparable and additionally are more easily applicable for post-processing.
There exist several univariate calibration
methods that transform two-class membership values into membership probabilities. A common approach is to apply the logistic regression
approach by Platt (1999). Zadrozny and Elkan (2002) supply an alternative method by using isotonic regression.
Multivariate extensions for regularization methods usually use a reduction to binary tasks, followed by univariate calibration and further application of the pairwise coupling algorithm by Hastie and Tibshirani (1998). An alternative method, the Dirichlet calibration, is introduced by Gebel and Weihs (2008).
Classification
Classification may refer to:* Library classification and classification in general* Taxonomic classification * Biological classification of organisms* Medical classification* Scientific classification...
, class membership probabilities reflect the uncertainty with which a given indivual item can be assigned to any given class. Although statistical classification methods by definition generate such probabilities, applications of classification in machine learning
Classification in machine learning
In machine learning and pattern recognition, classification refers to an algorithmic procedure for assigning a given piece of input data into one of a given number of categories...
usually supply membership values that do not induce any probabilistic confidence. It is desirable, to transform or re-scale membership values to class membership probabilities, since they are comparable and additionally are more easily applicable for post-processing.
There exist several univariate calibration
Calibration (statistics)
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Thus "calibration" can mean...
methods that transform two-class membership values into membership probabilities. A common approach is to apply the logistic regression
Logistic regression
In statistics, logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression...
approach by Platt (1999). Zadrozny and Elkan (2002) supply an alternative method by using isotonic regression.
Multivariate extensions for regularization methods usually use a reduction to binary tasks, followed by univariate calibration and further application of the pairwise coupling algorithm by Hastie and Tibshirani (1998). An alternative method, the Dirichlet calibration, is introduced by Gebel and Weihs (2008).