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

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Journal article

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International Journal of Intelligent Systems

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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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Copyright © 2001 John Wiley & Sons, Inc

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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