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Discover dependency pattern among attributes by using a new type of nonlinear multiregression
Author(s) -
Xu Kebin,
Wang Zhenyuan,
Wong ManLeung,
Leung KwongSak
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1043
Subject(s) - categorical variable , dependency (uml) , computer science , set (abstract data type) , nonlinear system , artificial intelligence , choquet integral , function (biology) , binary number , population , data mining , machine learning , mathematics , demography , arithmetic , quantum mechanics , evolutionary biology , sociology , biology , programming language , fuzzy logic , physics
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. © 2001 John Wiley & Sons, Inc.16: 949–962 (2001)