Name
Title | ||
|---|---|---|
Data streams classification by incremental rule learning with parameterized generalization |
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 neighbor 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 | DOI | Venue |
|---|---|---|
2006 | 10.1145/1141277.1141428 | SAC |
Keywords | Field | DocType |
clustering,decision rule,classification system,data consistency,data streams,nearest neighbor | Decision rule,k-nearest neighbors algorithm,Forgetting,Data stream mining,Parameterized complexity,Heuristic,Semi-supervised learning,Computer science,Artificial intelligence,Cluster analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-108-2 | 7 | 0.58 |
References | Authors | |
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 |