Discover dependency pattern among attributes by using a new type of nonlinear multiregression
Document Type
Journal article
Source Publication
International Journal of Intelligent Systems
Publication Date
1-1-2001
Volume
16
Issue
8
First Page
949
Last Page
962
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.
DOI
10.1002/int.1043
Print ISSN
08848173
E-ISSN
1098111X
Publisher Statement
Copyright © 2001 John Wiley & Sons, Inc
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Xu, K., Wang, Z., Wong, M.-L., & Leung, K.-S. (2001). Discover dependency pattern among attributes by using a new type of nonlinear multiregression. International Journal of Intelligent Systems, 16(8), 949-962. doi: 10.1002/int.1043