Class membership probabilities

Class membership probabilities

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).[1] Zadrozny and Elkan (2002)[2] supply an alternative method by using isotonic regression.

Multivariate extensions for regularization methods usually[citation needed] use a reduction to binary tasks, followed by univariate calibration and further application of the pairwise coupling algorithm by Hastie and Tibshirani (1998).[3] An alternative method, the Dirichlet calibration, is introduced by Gebel and Weihs (2008).[4]

References

  1. ^ J. C. Platt, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods". In: A. J. Smola, P. Bartlett, B. Schölkopf and D. Schuurmans (eds.), Advances in Large Margin Classiers, 61–74. Cambridge, MIT Press, 1999.
  2. ^ B. Zadrozny and C. Elkan, Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining , 694-699, Edmonton, ACM Press, 2002.
  3. ^ T. Hastie and R. Tibshirani, "Classification by pairwise coupling". In: M. I. Jordan, M. J. Kearns and S. A. Solla (eds.), Advances in Neural Information Processing Systems, volume 10, Cambridge, MIT Press, 1998.doi:10.1.1.46.6032
  4. ^ M. Gebel and C. Weihs, "Calibrating Margin–Based Classifier Scores into Polychotomous Assessment Probabilities". In: C. Preisach, H. Burkhardt, L. Schmidt-Thieme and R. Decker (Eds.), Data Analysis, Machine Learning and Applications, Springer, 29–36, 2008

Wikimedia Foundation. 2010.

Look at other dictionaries:

  • Latent class model — In statistics, a latent class model (LCM) relates a set of observed discrete multivariate variables to a set of latent variables. It is a type of latent variable model. It is called a latent class model because the latent variable is discrete. A… …   Wikipedia

  • Naive Bayes classifier — A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes theorem with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be independent feature model . In… …   Wikipedia

  • Conditional probability — The actual probability of an event A may in many circumstances differ from its original probability, because new information is available, in particular the information that an other event B has occurred. Intuition prescribes that the still… …   Wikipedia

  • Classification in machine learning — See also: Pattern recognition This section needs integrating with Statistical classification (Discuss). Integration means cross linking and distinguishing (to/from each other), or sometimes merging (if consensus suggests). In machine learning and …   Wikipedia

  • Mixture model — See also: Mixture distribution In statistics, a mixture model is a probabilistic model for representing the presence of sub populations within an overall population, without requiring that an observed data set should identify the sub population… …   Wikipedia

  • education — /ej oo kay sheuhn/, n. 1. the act or process of imparting or acquiring general knowledge, developing the powers of reasoning and judgment, and generally of preparing oneself or others intellectually for mature life. 2. the act or process of… …   Universalium

  • Forcing (mathematics) — For the use of forcing in recursion theory, see Forcing (recursion theory). In the mathematical discipline of set theory, forcing is a technique invented by Paul Cohen for proving consistency and independence results. It was first used, in 1963,… …   Wikipedia

  • Pierre-Simon Laplace — Laplace redirects here. For the city in Louisiana, see LaPlace, Louisiana. For the joint NASA ESA space mission, see Europa Jupiter System Mission. Pierre Simon, marquis de Laplace Pierre Simon Laplace (1749–1827). Posthumous portrait …   Wikipedia

  • operations research — the analysis, usually involving mathematical treatment, of a process, problem, or operation to determine its purpose and effectiveness and to gain maximum efficiency. [1940 45, Amer.] * * * Application of scientific methods to management and… …   Universalium

  • mathematics — /math euh mat iks/, n. 1. (used with a sing. v.) the systematic treatment of magnitude, relationships between figures and forms, and relations between quantities expressed symbolically. 2. (used with a sing. or pl. v.) mathematical procedures,… …   Universalium

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”