[D] Is this considered unsupervised or semi-supervised learning in anomaly detection?

Hi 👋🏼, I’m working on an anomaly detection setup and I’m a bit unsure how to correctly describe it from a learning perspective.

The model is trained using only one class of data (normal/benign), without using any labels during training. In other words, the learning phase is based entirely on modelling normal behaviour rather than distinguishing between classes.

At evaluation time, I select a decision threshold on a validation set by choosing the value that maximizes the F1-score.

So the representation learning itself is unsupervised (or one-class), but the final decision boundary is chosen using labeled validation data.

I’ve seen different terminology used for similar setups. Some sources refer to this as semi-supervised, while others describe it as unsupervised anomaly detection with threshold calibration.

What would be the most accurate way to describe this setting in a paper without overclaiming?