A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
One class SVM for yeast regulation prediction
ACM SIGKDD Explorations Newsletter
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
Boosting SVM classifiers by ensemble
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features
Proceedings of the 2006 ACM symposium on Applied computing
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
On the Suitable Domain for SVM Training in Image Coding
The Journal of Machine Learning Research
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
International Journal of Systems Science
Approximate Controllability of Infinite Dimensional Systems of the n-th Order
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
Advanced Data Mining Techniques
Advanced Data Mining Techniques
A study on reduced support vector machines
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper proposes an approach to decision analysis for complex industrial process without enough knowledge of input-output model, which is based on the two-class SVM method. It first proposes a SVM Based Decision Analysis Model to improve the accuracy of determinant of whether a decision is acceptable/ unacceptable by verifying the `soft margin' of a SVM. This makes it only allow misclassification of only one class in a two-class classification. Then a granular-based approach is presented to solving this model. It is proved that this granular approach can reach an upper bound of the original SVM model. An algorithm then is presented to determine whether a decision is acceptable. According to our analysis and experiments, the two types of SVM have better accuracy on judging its target class then traditional SVM, and the granular-based SVM solving can reduce the running time.