Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Fuzzy discretization of feature space for a rough set classifier
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
The Journal of Machine Learning Research
Accurate on-line support vector regression
Neural Computation
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A novel feature selection approach and its application
CIS'04 Proceedings of the First international conference on Computational and Information Science
Discretization of continuous attributes in rough set theory and its application
CIS'04 Proceedings of the First international conference on Computational and Information Science
Radar emitter signal recognition based on feature selection algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Intra-pulse modulation recognition of unknown radar emitter signals using support vector clustering
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
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Rough set theory (RST) can mine useful information from a large number of data and generate decision rules without prior knowledge. Support vector machines (SVMs) have good classification performances and good capabilities of fault-tolerance and generalization. To inherit the merits of both RST and SVMs, a hybrid classifier called rough set support vector machines (RS-SVMs) is proposed to recognize radar emitter signals in this paper. RST is used as preprocessing step to improve the performances of SVMs. A large number of experimental results show that RS-SVMs achieve lower recognition error rates than SVMs and RS-SVMs have stronger capabilities of classification and generalization than SVMs, especially when the number of training samples is small. RS-SVMs are superior to SVMs greatly.