Abstract
By using a non-additive set function to describe the interaction among variables, a nonlinear non-negative multi-regression is established based on the Choquet integral with respect to the set function. We generalize this nonlinear model and propose a novel formalism that provides an effective and efficient reasoning procedure to perform information fusion, decision making, and medical diagnoses. In the formalism, a network structure and a number of Choquet integrals are used to represent the relationships among variables. We propose a new algorithm to learn the network structure and the regression parameters of Choquet integrals from training examples in databases. The algorithm is based on the minimum description length (MDL) principle and evolutionary programming (EP). We conduct a series of experiments to demonstrate the performance of our algorithm and estimate the effectiveness of the MDL metric and the genetic operators. The empirical results illustrate that our algorithm can successfully discover the target network structure and the regression parameter.
Original language | English |
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Title of host publication | SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218) |
Publisher | IEEE |
Pages | 2354-2359 |
ISBN (Print) | 0780347781 |
Publication status | Published - Oct 1998 |
Externally published | Yes |
Event | 1998 IEEE International Conference on Systems, Man, and Cybernetics - San Diego, United States Duration: 14 Oct 1998 → 14 Oct 1998 Conference number: 98CH36218 |
Publication series
Name | Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics |
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ISSN (Print) | 1062-922X |
Conference
Conference | 1998 IEEE International Conference on Systems, Man, and Cybernetics |
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Abbreviated title | SMC98 |
Country/Territory | United States |
City | San Diego |
Period | 14/10/98 → 14/10/98 |