Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Streaming Active Learning Strategies for Real-Life Credit Card Fraud
  Detection: Assessment and Visualization   Credit card fraud detection is a very challenging problem because of the
specific nature of transaction data and the labeling process. The transaction
data is peculiar because they are obtained in a streaming fashion, they are
strongly imbalanced and prone to non-stationarity. The labeling is the outcome
of an active learning process, as every day human investigators contact only a
small number of cardholders (associated to the riskiest transactions) and
obtain the class (fraud or genuine) of the related transactions. An adequate
selection of the set of cardholders is therefore crucial for an efficient fraud
detection process. In this paper, we present a number of active learning
strategies and we investigate their fraud detection accuracies. We compare
different criteria (supervised, semi-supervised and unsupervised) to query
unlabeled transactions. Finally, we highlight the existence of an
exploitation/exploration trade-off for active learning in the context of fraud
detection, which has so far been overlooked in the literature.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org