Title & Authors
Jhun, Myoung-Shic; Choi, In-Kyung;

Abstract
The $\small{{\kappa}}$-Nearest Neighbors Classification(KNNC) is a popular non-parametric classification method which assigns a fixed number $\small{{\kappa}}$ 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;$\small{{\kappa}}$-nearest neighbors;
Language
Korean
Cited by
1.
수정된 적응 최근접 방법을 활용한 판별분류방법에 대한 연구,맹진우;방성완;전명식;

응용통계연구, 2010. vol.23. 6, pp.1093-1102
2.
순차 적응 최근접 이웃을 활용한 결측값 대치법,박소현;방성완;전명식;

응용통계연구, 2011. vol.24. 6, pp.1249-1257
1.
On the Use of Modified Adaptive Nearest Neighbors for Classification, Korean Journal of Applied Statistics, 2010, 23, 6, 1093
2.
On the Use of Sequential Adaptive Nearest Neighbors for Missing Value Imputation, Korean Journal of Applied Statistics, 2011, 24, 6, 1249
References
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

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

4.
Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Prentice Hall, New York