Vector projection method for unclassifiable region of support vector machine
Expert Systems with Applications: An International Journal
Orientation distance-based discriminative feature extraction for multi-class classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
Automatic sleep staging from ventilator signals in non-invasive ventilation
Computers in Biology and Medicine
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
Information Sciences: an International Journal
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Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.