Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.

中文翻译：

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基于改进SVM算法的平衡二叉决策树多分类研究

支持向量机（SVM）最初旨在解决二进制分类问题，但在现实世界中，存在许多多重分类的情况。基于支持向量机的多元分类方法主要分为直接分类法和间接分类法，其中由多个二进制分类器组成的多元分类法是按照一定的规则整合而成的多元分类法。展示。提出了一种基于平衡二叉决策树的改进的多分类算法，称为IBDT-SVM算法。在这种算法中 它不仅考虑了“类间距离”和“类方差”在传统的类间可分离性度量中的影响，而且考虑了“类间差异”，并提出了一种新的改进的“类间可分离性度量”。基于新的“类间可分离性度量”，它找出了类间可分离性度量最大的两个类，并将它们用作正样本和负样本来训练和学习分类器。此后，根据多数类分组的原则，其余类别接近这两个类别，并合并为正样本和负样本，以再次训练SVM分类器。对于分布不均或稀疏的样品，该方法可以避免最短距离距离分类法所引起的误差，最大程度地克服了传统二叉决策树中存在的“误差积累”问题，从而获得更好的分类器。根据上述算法，遍历决策树的每个层节点，直到输出分类结果为单类标签为止。实验结果表明，本文提出的IBDT-SVM算法可以较好地解决分类问题。遍历决策树的每个层节点，直到输出分类结果为单类标签。实验结果表明，本文提出的IBDT-SVM算法可以较好地解决分类问题。遍历决策树的每个层节点，直到输出分类结果为单类标签。实验结果表明，本文提出的IBDT-SVM算法可以较好地解决分类问题。