基于混合多距离度量的多分类器加权集成研究

在机器学习领域，分类器加权在小样本数据集中的分类正确率较低。为此，提出一种基于混合距离度量的多分类器加权集成方法。结合欧氏距离、曼哈顿距离、切比雪夫距离，设计混合的距离度量加权方法，使用加权投票组合规则集成各分类器的输出结果。实验结果表明，该方法鲁棒性较好，分类正确率较高。

Research on Weighted Integration of Multiple Classifier Based on Hybrid Multiple Distance Measurement

Aiming at the problem that the low classification accuracy rate of small sample in machine learning, this paper proposes a weighted integration of multiple classifiers method based on Hybrid Multiple Distances(HMD) measurement. In this method, three measure methods are combined, and the output of each classifier is integrated by weighted majority voting. It designs the mixed distance measure weighted method, and uses the weighted voting combination rules to integrate the classifier output results. Experimental results show that this method has good robustness and high classification accuracy.