Please use this identifier to cite or link to this item: 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:Applied Data Science - Publication

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