  In many real-world classification or recognition tasks, it is often difficult
to collect training examples that exhaust all possible classes due to, for
example, incomplete knowledge during training or ever changing regimes.
Therefore, samples from unknown/novel classes may be encountered in
testing/deployment. In such scenarios, the classifiers should be able to i)
perform classification on known classes, and at the same time, ii) identify
samples from unknown classes. This is known as open-set recognition. Although
random forest has been an extremely successful framework as a general-purpose
classification (and regression) method, in practice, it usually operates under
the closed-set assumption and is not able to identify samples from new classes
when run out of the box. In this work, we propose a novel approach to enabling
open-set recognition capability for random forest classifiers by incorporating
distance metric learning and distance-based open-set recognition. The proposed
method is validated on both synthetic and real-world datasets. The experimental
results indicate that the proposed approach outperforms state-of-the-art
distance-based open-set recognition methods.
