C4.5: programs for machine learning
C4.5: programs for machine learning
Comparing connectionist and symbolic learning methods
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Rule extraction by successive regularization
Neural Networks
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Symbolic Rule Extraction from the DIMLP Neural Network
Hybrid Neural Systems, revised papers from a workshop
A partial order for the M-of-N rule-extraction algorithm
IEEE Transactions on Neural Networks
A neural-network model for learning domain rules based on its activation function characteristics
IEEE Transactions on Neural Networks
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In this work the purpose is to determine discriminant hyperplanes of neural network in order to extract possible valuable knowledge by means of symbolic rules. We define a special neural network model denoted to as Discretized Interpretable Multi Layer Perceptron (DIMLP). As a result, rules are extracted in polynomial time with resoect to the size of the problem and the size of the network. Further, the degree of matching between extracted rules and neural networks responses is 100% Our network model was tested on 7 classification problems of the public domain. It turned out that DIMLPs were significantly more accurate than C4.5 decision trees on average.