The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Rough Set-Aided Feature Selection for Automatic Web-Page Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Neural Computation
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
An improved clustering algorithm for information granulation
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The generalization error of the symmetric and scaled support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Concept analysis via rough set and AFS algebra
Information Sciences: an International Journal
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Rough ν-support vector regression
Expert Systems with Applications: An International Journal
A Maximum Class Distance Support Vector Machine-Based Algorithm for Recursive Dimension Reduction
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A CBR-based fuzzy decision tree approach for database classification
Expert Systems with Applications: An International Journal
Fuzzy Aggregation with Artificial Color filters
Information Sciences: an International Journal
FRSVMs: Fuzzy rough set based support vector machines
Fuzzy Sets and Systems
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Information Sciences: an International Journal
Two-level hierarchical combination method for text classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock price movement prediction using representative prototypes of financial reports
ACM Transactions on Management Information Systems (TMIS)
Rough margin based core vector machine
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A weighted twin support vector regression
Knowledge-Based Systems
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Information Sciences: an International Journal
Recognizing architecture styles by hierarchical sparse coding of blocklets
Information Sciences: an International Journal
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By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical @n-SVM.