Name
Abstract | ||
|---|---|---|
In this paper, we present a new ensemble method, the Scale-free Network Classifier (SFNClassifier), that is conceived as a dynamic sized scale-free network. In Data Stream Mining, ensemble-based approaches have been proposed to enhance accuracy and allow fast recovery from concept drift. However, these approaches are based on both update and polling heuristics that do not present good accuracy results in arbitrary domains and do not represent explicitly the similarity between classifiers. The representation of the ensemble as a network allows us to extract centrality metrics, which are used to perform a weighted majority vote, where the weight of a classifier is proportional to its centrality value. Based on empirical studies, we concluded that SFNClassifier has comparable results to other ensemble-learners in terms of accuracy and outperformed the other methods in processing time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2554850.2554855 | SAC |
Keywords | Field | DocType |
algorithms,concept learning,data stream mining,ensemble classifiers,induction,data mining,classification,concept drift,social network analysis | Data mining,Data stream mining,Computer science,Social network analysis,Polling,Centrality,Concept drift,Heuristics,Artificial intelligence,Classifier (linguistics),Majority rule,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.45 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean Paul Barddal | 1 | 140 | 16.77 |
| Heitor Murilo Gomes | 2 | 155 | 17.36 |
| Fabrício Enembreck | 3 | 274 | 38.42 |