The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Advances in Instance Selection for Instance-Based Learning Algorithms
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Citations
Cites
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Kolodner, J. L. (2005) · The Cambridge Handbook of the Learning Sciences · doi:10.1017/cbo9780511816833.015 · Cited by 4,247
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
Instance-based learning algorithms
Aha, D. W., Kibler, D., & Albert, M. K. (1991) · Machine Learning · doi:10.1007/bf00153759 · Cited by 2,163
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
Wilson, D. L. (1972) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1972.4309137 · Cited by 2,130
The condensed nearest neighbor rule (Corresp.)
Hart, P. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054155 · Cited by 1,733
Nearest neighbor (NN) norms: NN pattern classification techniques
Dasarathy, B. V. (1991) · Medical Entomology and Zoology · Cited by 1,654
The reduced nearest neighbor rule (Corresp.)
Gates, G. (1972) · IEEE Transactions on Information Theory · doi:10.1109/tit.1972.1054809 · Cited by 566
An Experiment with the Edited Nearest-Neighbor Rule
(1976) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1976.4309523 · Cited by 470
Cited by
Toward integrating feature selection algorithms for classification and clustering
Liu, H., & Yu, L. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.66 · Cited by 2,706
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883
“Instead of training the network with all the data set, we can preselect a smaller but representative data set from the full training distribution set using instance selection methods [95] or Monte Carlo sampling [48].”
MethodMachine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
A survey on data preprocessing for data stream mining: Current status and future directions
Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.078 · Cited by 502
Metadata
- Authors
- Brighton, H., & Mellish, C.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2002-04
- Volume
- 6
- Issue
- 2
- Pages
- 153-172
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2158724449
- Cited by
- 532
- References
- 23
- FWCI
- 13.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Brighton, H., & Mellish, C. (2002). Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery. https://doi.org/10.1023/a%3A1014043630878
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
