改进PM-MD的分类器集成

Hub会对高维数据分析产生显著消极影响，现有研究分别采用了五种降Hubness策略以提高分类效果，但单个降Hubness策略适用范围有限.为解决这一问题，提出对五种降Hub分类器进行基于PM-MD的集成，PM-MD集成是通过KNN确定分类对象的决策表以及通过分类器得到分类对象的类支持向量，最后通过比较决策表和类支持向量的相似性计算分类器的竞争力权重.由于PM-MD在处理高维数据集时高斯势函数存在弱化距离导致区分度不足的倾向，因此提出了采用欧氏距离直接计算决策表以提高分类精度.在12个UCI数据集上的实验结果表明：PM-MD不仅获得更好且稳定的分类结果，而改进后的PM-MD则进一步提高了分类精度.

Improved PM-MD Classifier Integration

Hubs have a significant negative impact on high-dimensional data analysis. The current research uses five kinds of Hubness strategies to improve the classification effect, but each strategy has a limited scope of application. In order to solve this problem, PM-MD-based integration is proposed for the Hubness-based classifiers. The PM-MD integration determines a decision profile of the classification object through KNN and determines a class support vector of the classification object through the classifier. Finally, the competitiveness of the classifier integration is evaluated by comparing the similarity between the decision profile and the class support vector. When PM-MD dealing with high-dimensional data sets, because the Gaussian potential function tends to reduce the distance which leads to a lack of discrimination, it is proposed to use Euclidean distance to directly calculate the decision profile to improve the classification accuracy. The experimental results on 12 UCI datasets show that PM-MD obtains sound and stable classification results, and the improved PM-MD further improves the classification accuracy.