Paper Info
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. (C) 2001 John Wiley & Sons, Inc.
Year
DOI
Venue
2001
10.1002/int.1043
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Field
DocType
Volume
Set function,Data mining,Population,Evolutionary algorithm,Categorical variable,Regression analysis,Artificial intelligence,Choquet integral,Adaptive algorithm,Genetic algorithm,Machine learning,Mathematics
Journal
16
Issue
ISSN
Citations 
8
0884-8173
16
PageRank 
References 
Authors
1.99
11
4
Name
Order
Citations
PageRank
Kebin Xu114913.43
Zhenyuan Wang268490.22
Man-Leung Wong364451.23
Kwong-Sak Leung41887205.58