Discover dependency pattern among attributes by using a new type of nonlinear multiregression

Kebin XU, Zhenyuan WANG, Man Leung WONG, Kwong Sak LEUNG

Research output: Journal PublicationsJournal Article (refereed)

23 Citations (Scopus)

Abstract

Multiregression is one of the most common approaches used to discover dependency pattern among attributes in a database. Nonadditive set functions have been applied to deal with the interactive predictive attributes involved, and some nonlinear integrals with respect to nonadditive set functions are employed to establish a nonlinear multiregression model describing the relation between the objective attribute and predictive attributes. The values of the nonadditive set function play a role of unknown regression coefficients in the model and are determined by an adaptive genetic algorithm from the data of predictive and objective attributes. Furthermore, such a model is now improved by a new numericalization technique such that the model can accommodate both categorical and continuous numerical attributes. The traditional dummy binary method dealing with the mixed type data can be regarded as a very special case of our model when there is no interaction among the predictive attributes and the Choquet integral is used. When running the algorithm, to avoid a premature during the evolutionary procedure, a technique of maintaining diversity in the population is adopted. A test example shows that the algorithm and the relevant program have a good reversibility for the data.
Original languageEnglish
Pages (from-to)949-962
Number of pages14
JournalInternational Journal of Intelligent Systems
Volume16
Issue number8
DOIs
Publication statusPublished - 1 Jan 2001

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Attribute
Adaptive algorithms
Adaptive Genetic Algorithm
Choquet Integral
Reversibility
Genetic algorithms
Regression Coefficient
Categorical
Model
Nonlinear Model
Binary
Unknown
Interaction

Cite this

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title = "Discover dependency pattern among attributes by using a new type of nonlinear multiregression",
abstract = "Multiregression is one of the most common approaches used to discover dependency pattern among attributes in a database. Nonadditive set functions have been applied to deal with the interactive predictive attributes involved, and some nonlinear integrals with respect to nonadditive set functions are employed to establish a nonlinear multiregression model describing the relation between the objective attribute and predictive attributes. The values of the nonadditive set function play a role of unknown regression coefficients in the model and are determined by an adaptive genetic algorithm from the data of predictive and objective attributes. Furthermore, such a model is now improved by a new numericalization technique such that the model can accommodate both categorical and continuous numerical attributes. The traditional dummy binary method dealing with the mixed type data can be regarded as a very special case of our model when there is no interaction among the predictive attributes and the Choquet integral is used. When running the algorithm, to avoid a premature during the evolutionary procedure, a technique of maintaining diversity in the population is adopted. A test example shows that the algorithm and the relevant program have a good reversibility for the data.",
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Discover dependency pattern among attributes by using a new type of nonlinear multiregression. / XU, Kebin; WANG, Zhenyuan; WONG, Man Leung; LEUNG, Kwong Sak.

In: International Journal of Intelligent Systems, Vol. 16, No. 8, 01.01.2001, p. 949-962.

Research output: Journal PublicationsJournal Article (refereed)

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AU - LEUNG, Kwong Sak

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AB - Multiregression is one of the most common approaches used to discover dependency pattern among attributes in a database. Nonadditive set functions have been applied to deal with the interactive predictive attributes involved, and some nonlinear integrals with respect to nonadditive set functions are employed to establish a nonlinear multiregression model describing the relation between the objective attribute and predictive attributes. The values of the nonadditive set function play a role of unknown regression coefficients in the model and are determined by an adaptive genetic algorithm from the data of predictive and objective attributes. Furthermore, such a model is now improved by a new numericalization technique such that the model can accommodate both categorical and continuous numerical attributes. The traditional dummy binary method dealing with the mixed type data can be regarded as a very special case of our model when there is no interaction among the predictive attributes and the Choquet integral is used. When running the algorithm, to avoid a premature during the evolutionary procedure, a technique of maintaining diversity in the population is adopted. A test example shows that the algorithm and the relevant program have a good reversibility for the data.

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