用排序相关与近邻法对单分类模型进行元分类，实现数据集/算法自动识别
Meta-classification of one-class classification models using ranking correlation and nearest neighbor
- 将单分类模型转化为正常性排序表示，用排名相关与近邻法进行分类
- 在数据集标签下准确率达高，也能区分不同算法（当数据集含同一类时）
- 可统一处理模型、数据集与排序的分类，适用于睡眠记录等多样本输入场景
机器学习技术已被广泛应用于各类问题，但将机器学习用于机器学习模型仍是未被探索的方向。为此，本文提出对单分类（OCC）模型进行元分类，因为所有机器学习模型均可近似为单分类模型。该方法将OCC模型表示为正常性排序，并利用最近邻与排名相关性度量进行分类。实验中，类别对应训练数据集、算法及超参数。当标签为数据集时，该方法表现优异；在训练数据集包含相同类别时，仍可有效区分算法。讨论指出，OCC模型分类本质上是将多个样本视为单一输入的数据集分类。实验展示了使用睡眠记录对数据集进行分类的效果。所提方法为分类OCC模型、数据集与排序提供了统一解决方案。源代码已公开于https://github.com/ToshiHayashi/ClassOCC。
原文摘要 · Abstract (English)
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.
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