A Simplex Coding Multi-class Boosting Optimizing Algorithm

doi:10.3969/j.issn.1006-2475.2015.08.025

Abstract

Abstract: The multi-classification problem was divided into multiple independent binary problems in existing image classification mechanism, numbers of class directly influenced demand sizes of binary classifier. The number of classes in image classification problem was very large, which led to long training time, high computing demand and high test cost. In order to effectively solve these problems, this paper designed a multi-class boosting optimizing algorithm based on simplex coding(SCOBoost). Firstly, based on simplex coding, combining with the least squares support vector machine (LS-SVM) objective function, this paper proposed multi-classification improvement goal based on simplex coding; secondly, selected the weak classifiers which are not associated with the number of classes as the kernel function, and used iterative methods of boosting to solve. Experiment results on different data sets showed, SCOBoost not only had higher classification performance, but also had lower algorithm complexity, fast test speed and training time which is not affected by the number of classes and so on.
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CLC Number:

TP393

ZHAO Chun-lan. A Simplex Coding Multi-class Boosting Optimizing Algorithm[J]. Computer and Modernization, 2015, 0(8): 121-126.

References

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