aiqbench analysis
This research explores meta-classification techniques for one-class classification (OCC) models, proposing a method to categorize ML algorithms based on training datasets using ranking correlation and nearest-neighbor metrics. By treating multiple samples as single inputs, the study offers a unified framework that could streamline model selection and analysis in machine learning workflows without requiring explicit class labels during initial testing phases.
Meta-classification of one-class classification models using ranking correlation and nearest neighbor
arXiv:2606.17858v1 Announce Type: new 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.
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 this https URL.
Machine Learning (cs.LG)
arXiv:2606.17858 [cs.LG]
(or arXiv:2606.17858v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17858 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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