The Mathematical Programming and the Rule Extraction from Layered Feed-forward Neural Networks

Authors

Rua-Huan Tsaih, Department of Management Information Systems, National Chengchi University, Taipei, TaiwanFollow
Hsiou-Wei Lin, Department of International Business, National Taiwan University, Taipei, TaiwanFollow
Wen-Chyan Ke, Department of International Business, National Taiwan University, Taipei, TaiwanFollow
Cheng-Chang Lee, Department of Management Information Systems, National Chengchi University, Taipei, TaiwanFollow

Document Type

Article

Abstract

We propose a mathematical programming methodology for identifying and examining regression rules extracted from layered feed-forward neural networks. The area depicted in the rule premise covers a convex polyhedron in the input space, and the adopted approximation function for the output value is a multivariate polynomial function of x, the outside stimulus input. The mathematical programming analysis, instead of a data analysis, is proposed for identifying the convex polyhedron associated with each rule. Moreover, the mathematical programming analysis is proposed for examining the extracted rules to explore features. An implementation test on bond pricing rule extraction lends support to the proposed methodology.

Recommended Citation

Tsaih, Rua-Huan; Lin, Hsiou-Wei; Ke, Wen-Chyan; and Lee, Cheng-Chang, "The Mathematical Programming and the Rule Extraction from Layered Feed-forward Neural Networks" (2003). ICEB 2003 Proceedings (Singapore). 55.
https://aisel.aisnet.org/iceb2003/55