Jun 16, 2026 · cs.LGJ/K move · Enter open · S save
Toshitaka Hayashi, Hamido Fujita, Dalibor Cimr, Richard Cimler+1
aFaculty of Science, University of Hradec Kralove, Hradecká 1285, Hradec Kralove, 50003, Czech Republic · bMalaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kampung Datuk Keramat, Kuala Lumpur, 54100, Malaysia · cRegional Research Center, Iwate Prefectural University, Sugo 152-52, Takizawa, 020-0693, Iwate, Japan
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.