Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes
Neural Processing Letters
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
Neural Information Processing
Ensemble approaches of support vector machines for multiclass classification
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naïve Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16,063 gene expression levels and produced higher accuracy than other methods.