next up previous
Next: The definition of Rule Up: No Title Previous: Conclusions

Knowledge Eliciting from Neural Networks


Abstract: This part shows, in short, what can be understood from a ANN trained as, e.g., in the previous parts. Understanding what is written in a black-box ANN is also called Knowledge Eliciting.

This part is structured into 2 sections.

The first section deals with the theoretical aspects of rule extraction (knowledge eliciting) from ANNs. Starting with the definition of rule extraction, this section then explains the inverse problem: rule implementation, or the transformation of a rule base into a ANN. With this platform, the rule extraction mechanism is easier understood. Next, a few popular rule extraction mechanisms are presented. These algorithms are then discussed, classified, and finally rule quality criteria emerge.

The second section presents the specific application implemented and simulated on a computer. This problem is the second part of the main application of the thesis: the extracted knowledge has to be used in a classroom for teaching purposes. This being the problem setting, the quality criteria have to change. Starting from a typical knowledge based ANN, a new tool is built, a sunsymoblic knowledge extraction tool for time-series prediction. Implementation details are given, then some evaluations are made and limitations are discussed. Experts are called to judge upon the system, some results are displayed, conclusions are drawn.

Alexandra Cristea
Tue Feb 9 20:20:27 JST 1999