A Nearest Hyperrectangle Learning Method
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Extracting Decision Rules from Sigmoid Kernel
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
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ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
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RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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Neurocomputing
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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Expert Systems with Applications: An International Journal
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Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
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Support vector machine (SVM) is applied to many research fields because of its good generalization ability and solid theoretical foundation. However, as the model generated by SVM is like a black box, it is difficult for user to interpret and understand how the model makes its decision. In this paper, a hyperrectangle rules extraction (HRE) algorithm is proposed to extract rules from trained SVM. Support vector clustering (SVC) algorithm is used to find the prototypes of each class, then hyperrectangles are constructed according to the prototypes and the support vectors (SVs) under some heuristic conditions. When the hyperrectangles are projected onto coordinate axes, the if-then rules are obtained. Experimental results indicate that HRE algorithm can extract rules efficiently from trained SVM and the number and support of obtained rules can be easily controlled according to a user-defined minimal support threshold.