The nature of statistical learning theory
The nature of statistical learning theory
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
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 least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Building multi-way decision trees with numerical attributes
Information Sciences: an International Journal
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
On linear separability of data sets in feature space
Neurocomputing
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Information Sciences: an International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IEEE Transactions on Information Technology in Biomedicine
A transitivity analysis of bipartite rankings in pairwise multi-class classification
Information Sciences: an International Journal
Vector projection method for unclassifiable region of support vector machine
Expert Systems with Applications: An International Journal
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
Support Vector Machine Training for Improved Hidden Markov Modeling
IEEE Transactions on Signal Processing
Two Criteria for Model Selection in Multiclass Support Vector Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
IEEE Transactions on Neural Networks
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
An approach to SWIR hyperspectral hand biometrics
Information Sciences: an International Journal
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In this paper, a new model named Multiclass Support Vector Machines with Vector-Valued Decision (M-SVMs-VVD) or VVD is proposed. The basic idea is to separate 2^a classes by a SVM hyperplanes in the feature space induced by certain kernels, where a is a finite positive integer. We start from a 2^a-class problem, and extend it to any-class problem by applying a hierarchical decomposition procedure. Compared with the existing SVM-based multiclass methods, the VVD model has two advantages. First, it reduces the computational complexity by using a small number of classifiers. Second, the feature space partition induced by the hyperplanes effectively eliminates the Unclassifiable regions (URs) that may affect the classification performance of the algorithm. Experimental comparisons with several state-of-the-art multiclass methods demonstrate that VVD maintains a comparable testing accuracy, while it improves the classification efficiency with less classifiers, a smaller number of support vectors (SVs), and shorter testing time.