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http://hdl.handle.net/20.500.11861/7655
Title: | Discover dependency pattern among attributes by using a new type of nonlinear multiregression |
Authors: | Xu, Kebin Wang, Zhenyuan Wong, Man-Leung Prof. LEUNG Kwong Sak |
Issue Date: | 2001 |
Source: | International Journal of Intelligent Systems, 2001, vol. 16 (8), pp. 949 - 962 |
Journal: | International Journal of Intelligent Systems |
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. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7655 |
ISSN: | 08848173 |
DOI: | 10.1002/int.1043 |
Appears in Collections: | Publication |
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