一种基于混淆矩阵的多分类任务准确率评估新方法

A new multi-classification task accuracy evaluation method based on confusion matrix

多分类任务准确率评估对评判模型的分类效果具有重要的理论意义和应用价值。针对机器学习领域的多分类任务,在现有方法的基础上,通过拓展和迁移应用,给出一种新的评估方法。为了准确评估多分类任务模型的分类效果,将遥感图像分类效果评估方法引入多分类任务。针对多分类任务的实际特点,对该方法进行了改进与推广,以更好地评估分类器效能。基于MNIST手写字符集识别任务和CIFAR-10数据集分类任务的实验结果表明,同样是基于混淆矩阵进行计算,与现有的评估方法相比,该方法可以同时给出分类器整体的分类效果和单个类别的分类效果,对于改进训练过程有一定的指导意义。另一方面,该方法可以推广到任意的分类任务分类效果评估工作中,具有较好的应用前景。

The accuracy evaluation of multi-classification tasks has important theoretical significance and application value to the classification effect of the evaluation model.Aiming at multi-classification tasks in the field of machine learning,based on the current existing methods,this paper proposes a new method by expanding and migrating applications.In order to accurately evaluate the classification effect of the multi-classification task model,this paper introduces the remote sensing image classification effectiveness evaluation method(R′)into the multi-classification tasks.In view of the actual characteristics of the multi-classification tasks,the method improves and popularizes the R′method to better evaluate the performance of classifiers.The experimental results on the recognition task of MNIST handwritten character set and the classification task of the CIFAR-10 dataset show that,although the calculation is also based on the confusion matrix,compared with the existing evaluation indicators,the method can simultaneously give the overall classification performance of the classifiers and the classification efficiency of the individual categories,which can be beneficial to the training process.On the other hand,the method can be extended to the classification performance evaluation of any classification tasks,which has a good application prospect.