Machine Learning (ML) techniques have been applied to various problems. However, applying ML to ML models is an unexplored direction. For this purpose, this paper considers a meta-classification of one-class classification (OCC) models, because all ML models could be approximated as OCC models. The proposal represents OCC models as normality rankings and classifies them using nearest-neighbor and ranking-correlation metrics. The experiment classifies OCC models, where classes correspond to training datasets, algorithms, and hyperparameters. The proposal achieves high accuracy when class labels are datasets. Moreover, it can classify algorithms when the training datasets contain the same class. In addition, the discussion highlights that the classification of OCC models is essentially the classification of datasets that treats multiple samples as a single input. The experiment demonstrates the classification of datasets using sleeping records. The proposed method can provide a unified solution for classifying OCC models, datasets, and rankings. Source code is uploaded to the public repository https://github.com/ToshiHayashi/ClassOCC.

翻译：机器学习（ML）技术已广泛应用于各类问题，然而将机器学习应用于机器学习模型本身仍是一个未探索的研究方向。为此，本文提出一种针对一类分类（OCC）模型的元分类方法，其依据在于所有机器学习模型均可近似视为一类分类模型。该方案将一类分类模型表示为正态性排序，并采用最近邻与排名相关性度量对其进行分类。实验对一类分类模型进行类别划分，其中类别对应训练数据集、算法及超参数。当类别标签为数据集时，本方法实现了高准确率；此外，当训练数据集包含相同类别时，亦可完成算法分类。讨论部分进一步阐明，一类分类模型分类的本质是将多个样本作为单一输入的数据集分类任务。实验采用睡眠记录数据验证了数据集分类的有效性。所提方法可为一类分类模型、数据集及排序的分类提供统合解决方案。源代码已上传至公共仓库 https://github.com/ToshiHayashi/ClassOCC。