一种单一编码多分类 boosting 优化算法

摘要

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 pro-posed multi-classification improvement goal based on simplex coding;secondly, selected the weak classifiers which are not associ-ated with the number of classes as the kernel function, and used iterative methods of boosting to solve.Experiment results on dif-ferent 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.%现有图像分类机制一般将多类别分类问题划分成多个二类别分类问题的集合进行解决，类别数的多少直接影响着二值分类器的需求量。由于图像分类问题牵扯的类别数通常较多，从而导致其训练时间过长、计算需求过高以及测试代价过大等。针对上述问题，本文设计一种新型的多分类boosting优化算法，即SCOBoost。首先，以单一编码技术为基础，结合最小二乘支持向量机（ LS-SVM）目标函数，提出单一编码的多分类改进目标；其次，选取其数量与类别数无关联的弱分类器集合作为核函数，利用boosting的递归方式进行求解。通过对不同数据集实验，结果表明SCOBoost不仅拥有较高的分类性能，而且具有算法复杂度低、训练时间不受类别数影响、测试速率快等优点。