Neighborhood classifiers
Neighborhood classifiers
复制标题
邻域分类器
DOI：
10.1016/j.eswa.2006.10.043
复制
发表时间：
2008-02-01
影响因子：
8.5
通讯作者：
Me, Zongxia
中科院分区：
文献类型：
--
作者：
Hu, Qinghua;Yu, Daren;Me, Zongxia
K近邻分类器（K-NN）在模式识别和机器学习中得到了广泛的讨论和应用，然而，作为一种利用局部信息识别新的测试方法--邻域分类器的类似的惰性分类器，文献报道较少。该算法将属性约简技术与分类学习相结合。研究了这三种范数对属性约简和分类的影响，并将邻域分类器与KNN、CART和SVM进行了比较。实验结果表明，基于邻域的特征选择算法能够删除大部分冗余和不相关的特征。在原始特征空间和约简特征子空间中，邻域分类器的分类精度上级K-NN、CART，略弱于SVM。(c)2006爱思唯尔有限公司保留所有权利。
K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. This algorithm integrates attribute reduction technique with classification learning. We study the influence of the three norms on attribute reduction and classification, and compare neighborhood classifier with KNN, CART and SVM. The experimental results show that neighborhood-based feature selection algorithm is able to delete most of the redundant and irrelevant features. The classification accuracies based on neighborhood classifier is superior to K-NN, CART in original feature spaces and reduced feature subspaces, and a little weaker than SVM. (c) 2006 Elsevier Ltd. All rights reserved.