Knowledge Extraction from Trained Neural Network Scour Models

Knowledge Extraction from Trained Neural Network Scour Models

Knowledge Extraction From Trained Neural Networks

Received Jul 16 th , 2012 Revised Aug 01 th , 2012 Accepted Sept 02 th , 2012 Artificial neural networks (ANN) are very efficient in solving various kinds of problems But Lack of explanation capability (Black box nature of Neural Networks) is one of the most important reasons why artificial neural networks do not get necessary interest in some parts of industry. In this work artificial neural n...

Knowledge Extraction from Trained Neural Networks

The artificial neural networks (ANNs) are well suitable to solve a variety class of problems in a knowledge discovery field (e.g., in natural language processing) because the trained networks are more accurate at classifying the examples that represent a problem domain. However, the neural networks that consist of large number of weighted connections (called also links) and activation units oft...

An algorithm of knowledge extraction from trained neural networks

The presented paper describes a method of knowledge extraction that is based on analysis of the trained ANN's weights The method allows to determine the significance of particular inputs, to prove their synergy as well as to find some symbolic rules, that determine the direction of influence of particular inputs.

Symbolic knowledge extraction from trained neural networks: A sound approach

Although neural networks have shown very good performance in many application domains, one of their main drawbacks lies in the incapacity to provide an explanation for the underlying reasoning mechanisms. The “explanation capability” of neural networks can be achieved by the extraction of symbolic knowledge. In this paper, we present a new method of extraction that captures nonmonotonic rules e...