Meta-classification of one-class classification models using ranking correlation and nearest neighbor

2026-06-16

AI Summary

This study addresses the meta-classification problem of one-class classification (OCC) models, aiming to identify their corresponding training datasets, algorithms, or hyperparameters. To this end, the work proposes a unified representation of OCC models as normality rankings and reframes the meta-classification task as a ranking-based dataset classification problem. The authors develop a cohesive framework that integrates nearest-neighbor classifiers with rank correlation measures to jointly classify models, data, and rankings. Experimental results demonstrate that the proposed approach achieves high accuracy in dataset label classification and effectively distinguishes between OCC models trained with different algorithms within the same category. To the best of the authors’ knowledge, this is the first method to enable systematic identification of meta-information associated with OCC models.

Abstract

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.

Problem

• meta-classification
• one-class classification
• model classification
• dataset classification
• ranking correlation

Innovation

• meta-classification
• one-class classification
• ranking correlation
• nearest neighbor
• dataset classification