language: en

Evaluation and Ordering of Rules Extracted from Feedforward Networks

Ismail Taha and Joydeep Ghosh
Laboratory of Artificial Neural Systems University of Texas
Austin, TX. 78712-1084
{ismail,ghosh}@pine.ece.utexas.edu

Abstract Rules extracted from trained feedforward networks can be used for explanation, validation, and cross-referencing of network output decisions. This paper introduces a rule evaluation and ordering mechanism that orders rules extracted from feedforward networks based on three performance measures. Detailed experiments using three rule extraction techniques as applied to the Wisconsin breast cancer database, illustrate the power of the proposed methods. Moreover, a method of integrating the output decisions of both the extracted rule-based system and the corresponding trained network is proposed. The integrated system provides further improvements.

1. Introduction