# Is it better to do per-class anomaly detection on P(x, y) or P(x | y)?

(Not an expert in anomaly detection.)

I'd like to experiment with per-class anomaly detection.

That is, we have a feature vector $$x$$, and a classifier that predicts its class $$\hat{y}$$. I'd like to see if the combination $$(x, \hat{y})$$ is an anomaly, given some training set of non-anomalous $$(x, y)$$ pairs.

It seems that I can train one joint anomaly detector on $$P(x,y)$$, or multiple independent detectors on $$P(x|y)$$.

I think the latter is easier and sufficient. Are there any downsides? Also, is there a name for this technique?

• It is not clear, do you observe the class $y$, or do you predict it using a classifier? Dec 12, 2020 at 17:09
• The true class is observed at training time, and predicted when using the anomaly detector. It doesn't matter for your answer, though. Dec 13, 2020 at 5:44
• I'd say, that the predicted label $\hat y$ then simply aggregates information from $x$, adding nothing new, and there is no point in using it in normally detection. Dec 13, 2020 at 9:35

According to the Bayes law the joint probability of a sample observation $$(x,y)$$ can be decomposed into the familiar product:

$$P(x,y) = P(x|y)P(y).$$

In other words, we can always partition the set of features, $$(x,y)$$, into two subsets, $$x$$ and $$y$$, and then decompose the probability of the sample into the marginal probability of one subset $$P(y)$$ and the conditional probability of the other $$P(x|y)$$.

Note that the joint probability of the sample observation might fall below the anomaly threshold for two reasons:

• the probability of observed values for a subset of features, $$P(y)$$ is low, or
• the probability of observed values for the complementary subset, conditional on the values of the first subset, $$P(x|y)$$ is low.

When you only use the $$P(x|y)$$ model, you forego the information contained in the realization of $$y$$. In other words you implicitly agree with the observation of $$y$$, be it a modal (that's okay) or an extremely rare value (that's not okay, this already may be a sufficient reason for rejection).

• Thanks, I agree with your analysis. There's also an orthogonal consideration of whether P(x,y) or P(x|y) is easier to learn. I think usually, P(x|y) is simpler since it's not a mixture, but P(x,y) may be easier if the classes are imbalanced & there is reusable structure between the classes. Dec 13, 2020 at 5:54
• Yes, I like the point on the balance between classes. Depending on the size of your data, you can also face a trade-off: smaller models may be easier to train, but they may have worse performance due to insufficiency of training data. Dec 13, 2020 at 9:31