Learning nonlinear multiregression networks based on evolutionary computation

Kwong Sak LEUNG, Man Leung WONG, Wai LAM, Zhenyuan WANG, Kebin XU

Research output: Journal PublicationsJournal Article (refereed)peer-review

25 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)630-644
Number of pages15
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume32
Issue number5
DOIs
Publication statusPublished - 1 Jan 2002

Funding

This work was supported in part by the Research Grant Council of the Hong Kong Special Administrative Region under Grants Projects CUHK 4212/01E, CUHK 4187/01E, and LU 3012/01E, and in part by the Direct Grant Project 2050179 of the Engineering Faculty at the Chinese University of Hong Kong.

Keywords

  • Choquet integrals
  • Data mining
  • Evolutionary computation
  • Nonlinear multiregression networks

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