Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Accelerating neural network training using weight extrapolations
Neural Networks
Rule-extraction by backpropagation of polyhedra
Neural Networks
NeuroRule: A Connectionist Approach to Data Mining
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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SVM based rule extraction has become an important preprocessing technique for data mining, pattern classification, and so on. There are two key problems required to be solved in the classification rule extraction based on SVMs, i.e. the attribute importance ranking and the discretization to continuous attributes. In the paper, firstly, a new measure for determining the importance level of the attributes based on the trained SVR (Support vector re-gression) classifiers is proposed. Based on this new measure, a new approach for the division to continuous attribute space based on support vectors is pre-sented. A new approach for classification rule extraction from trained SVR classifiers is given. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the proposed ap-proach proposed can improve the validity of the extracted classification rules remarkably compared with other constructing rule approaches, especially for complicated classification problems.