A Hybrid Rule Extraction Method Using Rough Sets and Neural Networks

Abstract

Rough sets and neural networks are two common techniques applied to rule extraction from data table. Integrating the advantages of two approaches, this paper presents a Hybrid Rule Extraction Method (HREM) using rough sets and neural networks. In the HREM, the rule extraction is mainly done based on rough sets, while neural networks are only served as a tool to reduce the decision table and filter its noises when the final knowledge (rule sets) is generated from the reduced decision table by rough sets. Therefore, the HREM avoids the difficult of extracting rules from a trained neural network and possesses the robustness which the rough sets based approaches are lacking. The effectiveness of HREM is verified by comparing the experiment results with the approaches of traditional rough sets and neural networks.