A Local Mean-Based k-Nearest Centroid Neighbor Classifier
A Local Mean-Based k-Nearest Centroid Neighbor Classifier
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DOI：
10.1093/comjnl/bxr131
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发表时间：
2012-09-01
期刊：
影响因子：
1.4
通讯作者：
Xiong, Taisong
中科院分区：
文献类型：
--
作者：
Gou, Jianping;Yi, Zhang;Xiong, Taisong
KNN规则是一种简单有效的模式分类算法。在本文中，我们提出了一种基于局部均值的k-最近质心邻居分类器，该分类器为每个查询模式分配一个具有最近的局部质心均值向量的类标签，以提高分类性能。该方案不仅考虑了k个邻居的接近性和空间分布，而且利用每一类k个邻居的局部均值向量进行分类决策。在提出的分类器中，查询模式的每个类的k个最近质心邻居的局部平均向量被很好地定位，以充分捕获类分布信息。为了研究所提出的分类器的分类行为，我们在真实数据集和合成数据集上进行了大量的分类误差实验。实验结果表明，与最先进的基于knn的算法相比，我们提出的方法表现得非常好，特别是在小样本量的情况下。
K-nearest neighbor (KNN) rule is a simple and effective algorithm in pattern classification. In this article, we propose a local mean-based k-nearest centroid neighbor classifier that assigns to each query pattern a class label with nearest local centroid mean vector so as to improve the classification performance. The proposed scheme not only takes into account the proximity and spatial distribution of k neighbors, but also utilizes the local mean vector of k neighbors from each class in making classification decision. In the proposed classifier, a local mean vector of k nearest centroid neighbors from each class for a query pattern is well positioned to sufficiently capture the class distribution information. In order to investigate the classification behavior of the proposed classifier, we conduct extensive experiments on the real and synthetic data sets in terms of the classification error. Experimental results demonstrate that our proposed method performs significantly well, particularly in the small sample size cases, compared with the state-of-the-art KNN-based algorithms.