Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection: How to Artificially Increase your F1-Score with a
  Biased Evaluation Protocol   Anomaly detection is a widely explored domain in machine learning. Many
models are proposed in the literature, and compared through different metrics
measured on various datasets. The most popular metrics used to compare
performances are F1-score, AUC and AVPR. In this paper, we show that F1-score
and AVPR are highly sensitive to the contamination rate. One consequence is
that it is possible to artificially increase their values by modifying the
train-test split procedure. This leads to misleading comparisons between
algorithms in the literature, especially when the evaluation protocol is not
well detailed. Moreover, we show that the F1-score and the AVPR cannot be used
to compare performances on different datasets as they do not reflect the
intrinsic difficulty of modeling such data. Based on these observations, we
claim that F1-score and AVPR should not be used as metrics for anomaly
detection. We recommend a generic evaluation procedure for unsupervised anomaly
detection, including the use of other metrics such as the AUC, which are more
robust to arbitrary choices in the evaluation protocol.

27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an