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The Upper Preferred Multiple Directed Acyclic Graph Support Vector Machines for Classification |
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PP: 733-739 |
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Author(s) |
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Kan Li,
Hang Xu,
Wenxiong Huang,
Zhonghua Huang,
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Abstract |
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The current classification algorithms have weak fault-tolerance. In order to solve the problem, a multiple support vector
machines method, called Upper preferred Multiple Directed Acyclic Graph Support Vector Machines (UMDAG-SVMs), is proposed.
Firstly, we present least squares projection twin support vector machine (LSPTSVM) with confidence-degree for generating binary
classifiers. It uses the idea that “when the confidence-degree outputted from the node in the directed graph, is below the threshold,
the decision-making process will go on along with the two branches of the node at the same time.”, which strengthens the algorithm’s
fault-tolerance. In order to select the parameters of the algorithm, we use genetic algorithm to select these parameters. Secondly,
according to the minimal hypersphere distance, and the known principle “the upper-level classifiers bring up better performance of
classification in DAG-SVMs ”, we present a new classification algorithm, called UMDAG-SVMs. This algorithm has two advantages
of strong fault-tolerance and high classification accuracy. Finally, we make the experiments to test the performance of the algorithm.
Experimental results in public datasets show that our UMDAG-SVMs has comparable classification accuracy to that other algorithms
but with remarkable less computation. |
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