Rule Extraction from Trained Neural Network with Evolutionary Algorithms
Urszula Markowska–Kaczmar, Marcin Chumieja · 2003
This paper describes a solution to the problem of incomprehensibility of the neural network by introducing simultaneously working Evolutionary Algorithms as a tool for extracting set of rules in the form of if — then . Each Evolutionary Algorithm is working for searching rules describing one class, which is recognized by a Neural Network. The proposed method has been tested on real domains in order to analyze its behavior under various conditions. A comparison with other rule extraction methods is presented as well. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.