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- W847014716 abstract "비모수적 판별분류방법으로 널리 사용되는 <TEX>${kappa}$</TEX>-Nearest Neighbors Classification(KNNC) 방법은 자료의 국소적 특징을 고려하지 않고 전체 자료에 대해 고정된 이웃의 개수 <TEX>${kappa}$</TEX>를 사용하여 개체를 분류하는 방법이다. 본 연구에서는 KNNC의 대안으로 자료의 국소적 특징을 고려하는 Adaptive Nearest Neighbors Classificaion(ANNC) 방법을 제안하였다. 제안된 방법의 특징을 규명하기 위하여 실제 자료에 대한 분석을 통하여 제안된 방법의 응용 가능성을 제시하였으며, 나아가 모의실험을 통하여 기존의 방법과의 효율성을 비교하였다. The <TEX>${kappa}$</TEX>-Nearest Neighbors Classification(KNNC) is a popular non-parametric classification method which assigns a fixed number <TEX>${kappa}$</TEX> 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." @default.
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- W847014716 date "2009-06-30" @default.
- W847014716 modified "2023-10-17" @default.
- W847014716 title "Adaptive Nearest Neighbors for Classification" @default.
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- W847014716 doi "https://doi.org/10.5351/kjas.2009.22.3.479" @default.
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