Name
Abstract | ||
|---|---|---|
•A novel unsupervised approach to record linkage has been proposed.•The approach combines ensemble learning and automatic self learning.•An ensemble of diverse self learning models is generated through application of different string similarity metrics schemes.•Application of ensemble learning alleviates the problem of having to select the most suitable similarity metric scheme and improves the performance of an individual self learning model.•The proposed method obtained comparable results with the supervised methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.is.2017.06.006 | Information Systems |
Keywords | Field | DocType |
Unsupervised record linkage,Data matching,Classification,Ensemble learning | Data mining,Record linkage,Weighting,Active learning,Semi-supervised learning,Similarity measure,Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Ensemble learning,Machine learning | Journal |
Volume | ISSN | Citations |
71 | 0306-4379 | 3 |
PageRank | References | Authors |
0.39 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Jurek | 1 | 46 | 6.41 |
| Jun Hong | 2 | 5 | 1.44 |
| Yuan Chi | 3 | 3 | 0.39 |
| Weiru Liu | 4 | 1597 | 112.05 |