Extraction of Logical Rules from Neural Networks |
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Authors: | Duch Włodzisław Adamczak Rafał Grąbczewski Krzysztof |
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Affiliation: | (1) Department of Computer Methods, Nicholas Copernicus University, Grudzidzka 5, 87–100 Toru, Poland |
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Abstract: | Three neural-based methods for extraction of logical rules from data are presented. These methods facilitate conversion of graded response neural networks into networks performing logical functions. MLP2LN method tries to convert a standard MLP into a network performing logical operations (LN). C-MLP2LN is a constructive algorithm creating such MLP networks. Logical interpretation is assured by adding constraints to the cost function, forcing the weights to ±1 or 0. Skeletal networks emerge ensuring that a minimal number of logical rules are found. In both methods rules covering many training examples are generated before more specific rules covering exceptions. The third method, FSM2LN, is based on the probability density estimation. Several examples of performance of these methods are presented. |
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Keywords: | backpropagation feature selection logical rule extraction MLP neural networks probability density estimation |
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