Name
Abstract | ||
|---|---|---|
Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbour algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another. |
Year | Venue | Keywords |
|---|---|---|
2005 | JOURNAL OF UNIVERSAL COMPUTER SCIENCE | classification,decision rules,incremental learning,concept drift,data streams |
Field | DocType | Volume |
Decision rule,Nearest neighbour algorithm,Data mining,Forgetting,Heuristic,Data stream mining,Computer science,Incremental learning,Concept drift,Artificial intelligence,Machine learning | Journal | 11 |
Issue | ISSN | Citations |
8 | 0948-695X | 23 |
PageRank | References | Authors |
1.24 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco J. Ferrer-troyano | 1 | 69 | 8.97 |
| Jesús S. Aguilar-ruiz | 2 | 625 | 59.56 |
| José Cristóbal Riquelme Santos | 3 | 318 | 42.86 |