Decision-Tree-Based Support Vector Machines
Decision-Tree-Based Support Vector Machines
复制标题
基于决策树的支持向量机
DOI：
10.5687/iscie.16.125
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
S. Abe
中科院分区：
文献类型：
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作者：
Fumitake Takahashi;S. Abe
支持向量机（SVM）是一种具有很强泛化能力的模式识别方法。但是，由于传统的支持向量机最初制定的二进制分类问题，多类问题，区域中的数据不能分类存在于特征空间。在本文中，我们提出了基于决策树的支持向量机，其中我们递归地计算一个超平面，将一个类（或一些类）与其他类分开。这可以解决传统支持向量机存在不可分类区域的问题，但也带来了新的问题。即，特征空间的划分取决于决策树的结构。为了防止泛化能力的下降，更多的可分离类应该在决策树的上层分离。我们提出了四种类型的决策树，考虑到数据在输入空间中的分布。利用类中心之间的欧氏距离和马氏距离，我们确定了一个到其他决策树和一些类到其他决策树。我们使用基准数据集显示该算法的性能。
Support vector machines (SVMs) are known to have high generalization ability for pattern recognition. But since conventional SVMs are originally formulated for binary classification problems, for multiclass problems, the regions in which data cannot be classified exist in the feature space. In this paper, we propose decision-tree-based support vector machines, in which we recursively calculate a hyperplane that separates a class (or some classes) from the others. This can resolve the problem of the conventional SVMs, that is the existence of unclassifiable regions, but a new problem arises. Namely, the division of the feature space depends on the structure of a decision tree. To prevent degradation of generalization ability, more separable class should be separated atthe upper level of a decision tree. We propose four types of decision trees by taking into account the distribution of data in the input space. Using the Euclidean distances between class centers, and Mahalanobis distances, we determine one-to-the-others decision trees and some-classes-to-the-others decision trees. We show the performance of this algorithm using benchmark data sets.