A new general nearest neighbor classification based on the mutual neighborhood information
A new general nearest neighbor classification based on the mutual neighborhood information
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DOI：
10.1016/j.knosys.2017.01.021
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发表时间：
2017-04-01
影响因子：
8.8
通讯作者：
Ku, Weiping
中科院分区：
文献类型：
--
作者：
Pan, Zhibin;Wang, Yidi;Ku, Weiping
最近邻（NN）规则对于模式分类中的许多应用都是有效的，例如著名的k-最近邻（kNN）分类器。然而，基于NN的分类器通过简单地根据测试样本的邻域来寻找最近的邻居来执行单侧分类。在本文中，我们提出了一种新的选择最近邻的方法，称为一般最近邻（GNN）规则的基础上的双边模式。该方法充分考虑了测试样本和训练样本之间的互邻域信息，利用互邻域的重叠性来确定测试样本的广义最近邻。为了验证GNN规则在模式分类中的有效性，提出了一种k-广义最近邻（k-general nearest neighbor，kGNN）分类器。在UCI和KEEL数据库中的20个真实数据集和I-I和Ness数据集的两个高斯人工数据集上进行了大量的实验，证明了kGNN分类器的性能优于kNN分类器和其他7个最先进的基于NN的分类器，特别是在小训练样本量的情况下。(C)2017爱思唯尔B. V.保留所有权利。
The nearest neighbor (NN) rule is effective for many applications in pattern classification, such as the famous k-nearest neighbor (kNN) classifier. However, NN-based classifiers perform a one-sided classification by finding the nearest neighbors simply according to the neighborhood of the testing sample. In this paper, we propose a new selection method of nearest neighbors based on a two-sided mode, called general nearest neighbor (GNN) rule. The mutual neighborhood information of both testing sample and training sample is considered, then the overlapping of the above neighborhoods is used to decide the general nearest neighbors of the testing sample. To verify the effectiveness of the GNN rule in pattern classification, a k-general nearest neighbor (kGNN) classifier is proposed by applying the k-neighborhood information of each sample to find the general nearest neighbors. Extensive experiments on twenty real-world datasets from UCI and KEEL repository and two Gaussian artificial datasets of the I-I and Ness dataset prove that the kGNN classifier outperforms the kNN classifier and seven other state-of-the-art NN-based classifiers, particularly in the situations of small training sample size. (C) 2017 Elsevier B.V. All rights reserved.