Name
Abstract | ||
|---|---|---|
This work proposes a methodology based on Rough Sets and KDD for fraud detection made by electrical energy consumers. This methodology does a detailed evaluation of the boundary region between normal and fraudulent costumers, identifying patterns of fraudulent behavior at historical data sets of electricity companies. Using these patterns, classification rules are derived, and they will permit the detection on the database of electricity companies of those clients that present fraudulent feature. When doing inspections with the proposed methodology, the rate of correctness and the quantity of detected frauds are increased, decreasing the losses with electricity fraud on Brazilian electrical energy distribution companies. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/GRC.2006.1635791 | GrC |
Keywords | Field | DocType |
information systems,artificial intelligence,inspection,rough set,data mining,indexing terms,rough sets | Information system,Consumer unit,Computer security,Computer science,Electricity,Correctness,Rough set,Knowledge extraction,Business process discovery,Energy consumption | Conference |
ISBN | Citations | PageRank |
1-4244-0134-8 | 3 | 0.49 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Edison Cabral | 1 | 3 | 0.49 |
| João O. P. Pinto | 2 | 7 | 2.96 |
| Kathya S. C. Linares | 3 | 3 | 0.49 |
| Alexandra M. A. C. Pinto | 4 | 3 | 0.49 |