title: Nesting one-against-one algorithm based on SVMs for pattern classification.
abstract: 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.
authors.affiliation: College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China. csbliu@gmail.com
authors.forename: Bo
authors.initials: B
authors.lastname: Liu
authors.forename: Zhifeng
authors.initials: Z
authors.lastname: Hao
authors.forename: Eric C C
authors.initials: EC
authors.lastname: Tsang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*