从局部分类精度到分类置信度的变换

Transformation from Local Accuracy to Classification Confidence

基于局部分类精度设计多分类器系统能够有效地提高分类正确率．目前流行的动态分类器选择方法不能充分利用各个基本分类器的信息．在动态分类器选择方法中，局部分类精度最高的基本分类器决定最终的分类结果，其他基本分类器的信息被忽略．提出了一种将局部分类精度变换为分类置信度的方法，从而可以利用度量层分类器融合方法对得到的置信度进行融合．与动态分类器选择方法相比，度量层分类器融合方法能够利用更多的信息，从而能够取得更高的分类正确率．ELENA数据库、UCI数据库和DELVE数据库上的大量实验表明，新方法在分类正确率方面超过动态分类器选择方法大约0．2％～13．6％．

Local accuracy, which represents the accuracy of a base classifier for an input pattern, is one kind of valuable information used in the classifier combination methods. However, the only existing classifier combination method which uses local accuracy, namely dynamic classifier selection, can not take full advantage of the information from the base classifiers. In dynamic classifier selection, the final classification result is determined by the base classifier with the highest local accuracy, and the local accuracies of the other base classifiers are neglected. In this paper, a method of transforming local accuracy into classification confidence is proposed, in which the confidence value corresponding to the class outputted by a base classifier is proportional to the local accuracy of the base classifier, and the confidence values corresponding to the other classes are assumed to be equal. After the transformation, multiple classifier systems can be designed with measurement level classifier fusion methods such as linear combination. Compared with dynamic classifier combination, the classifier fusion methods, which make decisions by integrating the classification confidences of all the base classifiers, can use more information and achieve a higher classification rate. To evaluate this approach, a lot of experiments are conducted on six large data sets selected from the ELENA, UCI, and DELVE databases. The experimental results show that the approach outperforms dynamic classifier selection by 0.2% to 13.6% in classification rate.