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.