Abstract
Nearest Neighbor (NN) classification assumes class conditional probabilities to be locally constant, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighbor-based methods, which only analyze a small space emanating from the query sample, the proposed nearest neighbor classification using cam weighted distance (CamNN) optimizes the distance measure based on the analysis of the inter-prototype relationships. Experiments show that CamNN significantly outperforms one nearest neighbor classification (1-NN) and k-nearest neighbor classification (k-NN) in most benchmarks, while its computational complexity is competitive with 1-NN classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hart, P., Cover, T.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Devroye, L.: On the inequality of cover and hart in nearest neighbor discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 75–79 (1981)
Wagner, T.: Convergence of the nearest neighbor rule. IEEE Transactions on Information Theory 17(5), 566–571 (1971)
II Short, R., Fukunaga, K.: The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory 27(5), 622–627 (1981)
Domeniconi, C., Peng, J., Gunopulos, D.: Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1281–1285 (2002)
Friedman, J.H.: Flexible metric nearest neighbor classication. The Pennsylvania State University CiteSeer Archives. (September 24 1999)
Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(6), 607–616 (1996)
Hayes, R.R., Fukunaga, K.: Estimation of classifier performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(10), 1087–1101 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, C.Y., Chen, Y.Q. (2005). Nearest Neighbor Classification Using Cam Weighted Distance. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_14
Download citation
DOI: https://doi.org/10.1007/11540007_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)