An improved multiple birth support vector machine for pattern classification
An improved multiple birth support vector machine for pattern classification
复制标题
一种改进的模式分类多生支持向量机
DOI：
10.1016/j.neucom.2016.11.006
复制
发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Xue Yu
中科院分区：
文献类型：
--
作者：
Zhang Xiekai;Ding Shifei;Xue Yu
多生支持向量机是一种新的多类分类机器学习算法，是孪生支持向量机的一种扩展。与其他基于孪生支持向量机的多类分类器的训练速度相比，多生支持向量机的训练速度更快，特别是在类数较多的情况下。然而，多出生支持向量机的一个缺点是，当用于处理一些数据集时，如“交叉平面”数据集，多出生支持向量机可能会得到不好的结果。为了解决这一问题，我们提出了一种改进的多胎支持向量机。我们在多出生支持向量机中加入一个修正项，使给定类的每个样本到其超平面的距离的方差尽可能小。为了预测新样本，我们的方法首先根据训练样本与其超平面之间的距离为每个类确定一个区间，然后根据超平面与新样本之间的距离对新样本进行分类。此外，平滑技术应用于我们的模型，第一次将其用于多类孪生支持向量机。在人工数据集和UCI数据集上的实验结果表明，该算法是有效的，具有良好的分类性能。
Multiple birth support vector machine is a novel machine learning algorithm for multi-class classification, which is considered as an extension of twin support vector machine. Compared with training speeds of other multi-class classifiers based on twin support vector machine, the training speed of multiple birth support vector machine is faster, especially when the number of class is large. However, one of the disadvantages of multiple birth support vector machine is that when used to deal with some datasets such as “Cross planes” datasets, multiple birth support vector machine is likely to get bad results. In order to deal with this, we propose an improved multiple birth support vector machine. We add a modified item into multiple birth support vector machine to make the variance of the distances from each samples of a given class to their hyperplanes as small as possible. To predict a new sample, our method first determines an interval for each class depending on the distances between training samples and their hyperplanes, and then classifies the new sample depending on the distances between hyperplanes and the new sample which are in the corresponding intervals. In addition, smoothing technique is applied on our model, the first time it was used in multi-class twin support vector machine. The experimental results on artificial datasets and UCI datasets show that the proposed algorithm is efficient and has good classification performance.