Name
Title | ||
|---|---|---|
A copula based approach for measurement validity verification in wireless sensor networks. |
Abstract | ||
|---|---|---|
Outlier detection is the process of identifying the data objects that do not comply with the normal behavior of the defined data model. Used in automated data analysis, it ensures the desired data quality and reliability. This field has attracted increasing attention in the wireless sensor network domain, using methods from machine learning, data mining, and statistics. In this paper, we propose a novel outlier detection approach based on Copula theory. This powerful theory allows to model the dependency between data measurements in a formal and statistical way. We have evaluated our proposed approach with a real world dataset. Our results show a detection rate of 85.90% and an error rate of 0.87%. |
Year | Venue | Field |
|---|---|---|
2017 | ICC | Data mining,Anomaly detection,Data quality,Computer science,Copula (linguistics),Word error rate,Outlier,Data objects,Data model,Wireless sensor network |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
20 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanaa Kawther Ghalem | 1 | 0 | 0.68 |
| Kechar Bouabdellah | 2 | 32 | 9.58 |
| Ahcène Bounceur | 3 | 306 | 35.05 |
| Reinhardt Euler | 4 | 95 | 28.50 |
| Mohammad Hammoudeh | 5 | 225 | 36.88 |
| Farid Lalem | 6 | 21 | 4.54 |