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Neural methods of knowledge extraction

Duch W., Adamczak R., Grąbczewski K., Jankowski N.

Control and Cybernetics

2000

Vol. 29, no 4

997-1017

Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a new methodology of logical rule extraction, optimization and application of rule-based systems has been described. C-MLP2LN algorithm, based on constrained multilayer perceptron network, is described here in details and the dynamics of a transition from neural to logical system illustrated. The algorithm handles real-valued features, determining appropriate linguistic variables or membership functions as a part of the rule extraction process. Initial rules are optimized by exploring the accuracy/simplicity tradeoff at the rule extraction stage and the one between reliability of rules and rejection rate at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Comments are made on application of neural networks to knowledge discovery in the benchmark and real life problems.