Optimal discriminant analysis and classification tree analysis: Difference between revisions

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KW - and Statistical Techniques

PY - 1991

ER -1.tb00362.x</ref> and the related '''classification tree analysis''' ('''CTA''') are exact statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact [[Type I error]] rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to >&nbsp;0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. ClassificationOptimal treediscriminant analysis is aan generalization of optimal discriminant analysisalternative to non-orthogonal trees. Classification tree [[analysis hasof more recently been called "hierarchical optimal discriminantvariance|ANOVA]] (analysis". Optimalof discriminant analysisvariance) and classification tree[[regression analysis may be used to find the combination of variables and cut points that best separate classes of objects or events. These variables and cut points may then be used to reduce dimensions and to then build a statistical model that optimally describes the data]].

Optimal discriminant analysis may be thought of as a generalization of Fisher's [[linear discriminant analysis]]. Optimal discriminant analysis is an alternative to [[analysis of variance|ANOVA]] (analysis of variance) and [[regression analysis]], which attempt to express one [[dependent variable]] as a linear combination of other features or measurements. However, ANOVA and regression analysis give a dependent variable that is a numerical variable, while optimal discriminant analysis gives a dependent variable that is a class variable.

==See also==

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== References ==

<references/>

== Notes ==

* {{cite book

|title=Optimal Data Analysis

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* {{cite journal |last1=Martinez |first1=A. M. |last2=Kak |first2=A. C. |title=PCA versus LDA |journal=[[IEEE Transactions on Pattern Analysis and Machine Intelligence]] |volume=23 |issue=2 |pages=228&ndash;233 |year=2001 |url=http://www.ece.osu.edu/~aleix/pami01f.pdf |doi=10.1109/34.908974 }}{{Dead link|date=April 2020 |bot=InternetArchiveBot |fix-attempted=yes }}

* {{cite book

|author=Mika, S.|title=Fisher Discriminant Analysis with Kernels

|chapter=Fisher discriminant analysis with kernels

|journal=IEEE Conference on Neural Networks for Signal Processing IX

|title=Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)

|year=1999

|pages=41&ndash;48

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|display-authors=etal|isbn=978-0-7803-5673-3

|citeseerx=10.1.1.35.9904

|s2cid=8473401

}}

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*[https://web.archive.org/web/20140526130544/http://www.roguewave.com/portals/0/products/imsl-numerical-libraries/fortran-library/docs/7.0/stat/stat.htm IMSL discriminant analysis function DSCRM], which has many useful mathematical definitions.

[[Category:Statistical classification]]

[[Category:Classification algorithms]]

[[Category:Psychometrics]]

[[Category:Quantitative marketing research]]

[[de:Diskriminanzanalyse]]