  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.
