Minimum distance classifier ensemble based on adaptive distance metric
基于自适应距离度量的最小距离分类器集成

A minimum distance classifier ensemble method based on adaptive distance metric was proposed. The training method of component classifier was given. Some training subsets were obtained via bootstrap technique, then the model about adaptive distance metric with the training subset was established. Each component classifier was trained independently using the model, then some component classifiers were obtained. After that, they were collected to make a decision according to the majority voting. Experiment results on UCI standard database show that the proposed ensemble method based on adaptive distance metric for minimum distance classifier is effective, and it is superior to other methods in classification performance.