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Traditional classifiers infer labels under the premise that the training andtest samples are generated from the same distribution. This assumption can beproblematic for safety-critical applications such as medical diagnosis andnetwork attack detection. In this paper, we consider the multi-classclassification problem when the training data and the test data may havedifferent distributions. We propose conformalized semi-supervised random forest(CSForest), which constructs set-valued predictions $C(x)$ to include thecorrect class label with desired probability while detecting outliersefficiently. We compare the proposed method to other state-of-art methods inboth a synthetic example and a real data application to demonstrate thestrength of our proposal.

Conformalized semi-supervised random forest for classification and abnormality detection