An outlier generation approach for one-class random forests: An example in one-class classification of remote sensing imagery

Abstract

We propose a new outlier generation approach for one-class random forests (OCRF), a recently developed one-class classifier. The proposed method makes use of a positive and unlabeled learning (PUL) algorithm to generate outliers from the unlabeled samples. The outlier samples generated and the target samples are then used to train an OCRF classifier for one-class classification. The proposed method is evaluated using hyperspectral data, and the results showed that the OCRF with the proposed outlier generation method provides high classification accuracy, outperforming the original OCRF, PUL and OCSVM.