The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
KNN Model-Based Approach in Classification
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Citations
Cites
Neural networks for pattern recognition
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Neural Networks for Pattern Recognition
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Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,926
Hand, D. J., Mannila, H., & Smyth, P. (2001) · Cited by 2,581
The condensed nearest neighbor rule (Corresp.)
Hart, P. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054155 · Cited by 1,733
Smith, A. (2002) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(02)00058-1 · Cited by 1,700
Reduction Techniques for Instance-Based Learning Algorithms
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The reduced nearest neighbor rule (Corresp.)
Gates, G. (1972) · IEEE Transactions on Information Theory · doi:10.1109/tit.1972.1054809 · Cited by 566
Voting over Multiple Condensed Nearest Neighbors
Alpaydın, E. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006563312922 · Cited by 99
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- Wang, H., Duntsch, I., Bell, D.: Data Reduction Based on Hyper Relations. In: Proceedings of KDD 1998, New York, pp. 349–353 (1998)
- Kubat, M., Jr., M.: Voting Nearest-Neighbour Subclassifiers. In: Proceedings of the 17th International Conference on Machine Learning, ICML 2000, pp. 503–510, Stanford, CA, June 29-July 2 (2000)
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Cited by
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,495
“The explanation is based on the principle that the algorithm classifies a new data point based on the class labels of its nearest neighbos in the training dataset [125].”
BackgroundMachine 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
Efficient kNN classification with different numbers of nearest neighbors
Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2673241 · Cited by 1,378
A deep learning framework for audio deepfake detection
Khochare, J., Joshi, C., Yenarkar, B., Suratkar, S., & Kazi, F. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06297-w · Cited by 135
Islam, M. M., Rifat, H. R., Shahid, M. S. B., Akhter, A., Uddin, M. A., & Uddin, K. M. M. (2025) · Engineering Reports · doi:10.1002/eng2.13080 · Cited by 41
Metadata
- Authors
- Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2003
- Pages
- 986-996
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2136132422
- Cited by
- 2,099
- References
- 10
- FWCI
- 4.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN Model-Based Approach in Classification. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-39964-3_62
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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.
