One-Class Classification by Ensembles of Regression models -- a detailed study

TL;DR

This paper introduces OCCER, a novel one-class classification method that employs ensembles of regression models to identify outliers, demonstrating effectiveness on various datasets and compatibility with autoencoder-generated features.

Contribution

The paper presents OCCER, a new ensemble regression-based approach for OCC that transforms the problem into multiple regression tasks and leverages latent features from autoencoders.

Findings

OCCER outperforms state-of-the-art OCC algorithms on several datasets.

The method effectively utilizes autoencoder latent features for image data.

OCCER demonstrates robustness across different data types.

Abstract

One-class classification (OCC) deals with the classification problem in which the training data has data points belonging only to target class. In this paper, we study a one-class classification algorithm, One-Class Classification by Ensembles of Regression models (OCCER), that uses regression methods to address OCC problems. The OCCER coverts an OCC problem into many regression problems in the original feature space so that each feature of the original feature space is used as the target variable in one of the regression problems. Other features are used as the variables on which the dependent variable depends. The errors of regression of a data point by all the regression models are used to compute the outlier score of the data point. An extensive comparison of the OCCER algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the this approach. We also demonstrate that the OCCER algorithm can work well with the latent feature space created by autoencoders for image datasets. The implementation of OCCER is available at https://github.com/srikanthBezawada/OCCER.