International Journal of Man-Machine Studies
Neurocomputing
Hybrid architectures for intelligent systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
An inquiry into anatomy of conflicts
Information Sciences: an International Journal
A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
Pairwise classification and support vector machines
Advances in kernel methods
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Some mathematical structures for computational information
Information Sciences—Applications: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
Applying A Hybrid Method To Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Bounds on Error Expectation for Support Vector Machines
Neural Computation
On the structure of generalized rough sets
Information Sciences: an International Journal
Reducing the storage requirements of 1-v-1 support vector machine multi-classifiers
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Information Sciences: an International Journal
A rough margin based support vector machine
Information Sciences: an International Journal
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
A short note on algebraic T-rough sets
Information Sciences: an International Journal
Predicting Cytokines Based on Dipeptide and Length Feature
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Information Sciences: an International Journal
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
Rough ν-support vector regression
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Semi-supervised Rough Cost/Benefit Decisions
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
Rough multi-category decision theoretic framework
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
Voting based extreme learning machine
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Rough set theory applied to lattice theory
Information Sciences: an International Journal
An application of rough sets to graph theory
Information Sciences: an International Journal
Relevant feature selection from EEG signal for mental task classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A three phase approach for mental task classification using EEG
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Semi-supervised Rough Cost/Benefit Decisions
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Computing connected components of simple undirected graphs based on generalized rough sets
Knowledge-Based Systems
Axiomatic systems for rough set-valued homomorphisms of associative rings
International Journal of Approximate Reasoning
An efficient classification approach for large-scale mobile ubiquitous computing
Information Sciences: an International Journal
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
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Support vector machines (SVMs) are essentially binary classifiers. To improve their applicability, several methods have been suggested for extending SVMs for multi-classification, including one-versus-one (1-v-1), one-versus-rest (1-v-r) and DAGSVM. In this paper, we first describe how binary classification with SVMs can be interpreted using rough sets. A rough set approach to SVM classification removes the necessity of exact classification and is especially useful when dealing with noisy data. Next, by utilizing the boundary region in rough sets, we suggest two new approaches, extensions of 1-v-r and 1-v-1, to SVM multi-classification that allow for an error rate. We explicitly demonstrate how our extended 1-v-r may shorten the training time of the conventional 1-v-r approach. In addition, we show that our 1-v-1 approach may have reduced storage requirements compared to the conventional 1-v-1 and DAGSVM techniques. Our techniques also provide better semantic interpretations of the classification process. The theoretical conclusions are supported by experimental findings involving a synthetic dataset.