Browse > Article
http://dx.doi.org/10.5351/KJAS.2009.22.3.479

Adaptive Nearest Neighbors for Classification  

Jhun, Myoung-Shic (Department of Statistics, Korea University)
Choi, In-Kyung (Department of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.3, 2009 , pp. 479-488 More about this Journal
Abstract
The ${\kappa}$-Nearest Neighbors Classification(KNNC) is a popular non-parametric classification method which assigns a fixed number ${\kappa}$ of neighbors to every observation without consideration of the local feature of the each observation. In this paper, we propose an Adaptive Nearest Neighbors Classification(ANNC) as an alternative to KNNC. The proposed ANNC method adapts the number of neighbors according to the local feature of the observation such as density of data. To verify characteristics of ANNC, we compare the number of misclassified observation with KNNC by Monte Carlo study and confirm the potential performance of ANNC method.
Keywords
Adaptive nearest neighbors; classification analysis; ${\kappa}$-nearest neighbors;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Friedman, J. (1994). Flexible metric nearest-neighbor classification, Technical report, Standford University
2 Hastie, T. and Tibshrani, R. (1996). Discriminant adaptive nearest-neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 607-616   DOI   ScienceOn
3 Jhun, M., Jeong, H. C. and Koo, J. Y. (2007). On the use of adaptive nearest neighbors for missing value imputation, Communications in Statistics: Simulation and Computation, 36, 1275-1286   DOI   ScienceOn
4 Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Prentice Hall, New York