Name
Abstract | ||
|---|---|---|
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TSMCB.2002.1033182 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
optimisation,choquet integral,domain attributes,evolutionary computation,fast double optimization algorithm,particular network element,decision making,inference mechanisms,statistical analysis,learning (artificial intelligence),network structure,nonlinear interaction,prediction,global learning algorithm,nonlinear nonnegative multiregressions,regression coefficients,network-based model,network element,training data sets,nonlinear multiregression network learning,multiregression residual cost evolutionary programming,network elements,knowledge discovery,data mining,data mining framework,databases,local learning component,network structures,reasoning procedure,nonlinear interactions,nonlinear multiregression network,target network structure,single network element,linear model,predictive models,indexing terms,learning artificial intelligence,training data,genetic programming,evolutionary computing,terrorism | Mathematical optimization,Nonlinear system,Linear model,Computer science,Evolutionary computation,FDOA,Knowledge extraction,Artificial intelligence,Network element,Choquet integral,Evolutionary programming,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 5 | 1083-4419 |
Citations | PageRank | References |
16 | 1.08 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwong-Sak Leung | 1 | 1887 | 205.58 |
| Man-Leung Wong | 2 | 644 | 51.23 |
| Wai Lam | 3 | 1498 | 145.11 |
| Zhenyuan Wang | 4 | 684 | 90.22 |
| Kebin Xu | 5 | 149 | 13.43 |