Name
Abstract | ||
|---|---|---|
Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transa... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2736643 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Credit cards,Training,Learning systems,Tools,Amplitude modulation,Area measurement,Companies | Credit card fraud,Computational intelligence,Data stream,Computer security,Latency (engineering),Computer science,Concept drift,Credit card,Artificial intelligence,Business intelligence,Small set,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 8 | 2162-237X |
Citations | PageRank | References |
11 | 0.49 | 50 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Dal Pozzolo | 1 | 119 | 5.15 |
| Giacomo Boracchi | 2 | 324 | 30.49 |
| Olivier Caelen | 3 | 166 | 14.81 |
| Cesare Alippi | 4 | 1040 | 115.84 |
| Gianluca Bontempi | 5 | 1003 | 73.13 |