Extracting linguistic quantitative rules from supervised neural networks

Abstract

Extracting meaningful and understandable knowledge from a trained neural network is one of the ultimate goals in the area of data mining. In this paper, we propose a technique for extracting knowledge with less complex mathematical elaboration based on our activation interval projection on each dimensional axis with certainty factor refinement. The knowledge is captured in forms of if-then rules, which their premises are the conjunction of input feature intervals representing in linguistic quantities. Our experiment signifies that the extracted rules accurate when compared with those from a neural network.