Name
Abstract | ||
|---|---|---|
This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propose an approximate procedure based on discretizing both the possible probability values and the parameter representing the probability of change. The result is a method which is quite efficient in time and space (with a complexity directly related to the number of points used in the discretization) and providing very accurate predictions as well. These benefits are checked with a detailed comparison with other standard procedures based on variable size windows or forgetting rates. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.knosys.2019.104909 | Knowledge-Based Systems |
Keywords | Field | DocType |
Concept drift,Dynamic Bayesian networks,Change detection,Propagation algorithms | Discretization,Forgetting,Data mining,Computer science,Spacetime,Concept drift,Dynamic Bayesian network,Bayesian probability | Journal |
Volume | ISSN | Citations |
185 | 0950-7051 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Manuel Gómez-Olmedo | 2 | 61 | 11.98 |
| Serafin Moral | 3 | 95 | 13.82 |