自适应半径选择的近邻邻域分类器

doi:10.16451/j.cnki.issn1003-6059.202211004

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摘要

在邻域粗糙集中,邻域分类器简单高效.然而,邻域半径作为决定邻域分类器分类性能的关键因素,构建方式存在不足.一方面,邻域半径的构建由于未经过训练过程而缺乏通用性;另一方面,当数据样本分布不均出现空邻域时可导致分类器失效.针对上述问题,文中提出自适应半径选择的近邻邻域分类器(Near Neighborhood Classifier with Adaptive Radius Selection, NNC-AR).首先,基于K近邻算法为训练样本构建训练邻域半径.然后,为了克服传统方法选取邻域半径参数的主观性,对待测试样本定义自适应的近邻邻域半径.最后,为分类器失效的部分测试样本定义新的近似邻域半径,有效提升分类器的泛化能力.实验表明,NNC-AR的F1值和分类精度均较高.

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关键词 ：邻域粗糙集, 邻域分类器, 邻域半径, 自适应半径

Abstract：

In neighborhood rough sets, the neighborhood classifier is an intuitive and effective classification method in data mining. However, the neighborhood radius, as a key factor to determine the classification performance of the neighborhood classifier, has some defects in construction. The construction of the neighborhood radius is not universal due to the lack of the training stage. When the data samples are not uniformly distributed and then empty neighborhoods appear, the classifier fails. Aiming at these problems, a near neighborhood classifier with adaptive radius selection(NNC-AR) is proposed. Firstly, for training samples, a training neighborhood radius based on K-nearest neighbor is defined. For test samples, an adaptive neighborhood radius is defined to overcome the subjectivity of the artificial parameter in the traditional neighborhood radius. Finally, for the test samples with classifier failure, an approximate neighborhood radius is defined to improve the generalization ability of the classifier. The experimental results show that the F1 score and classification accuracy of NNC-AR model are significantly improved.

Key words： Key Words Neighborhood Rough Set Neighborhood Classifier Neighborhood Radius Adaptive Radius

收稿日期: 2022-09-29

ZTFLH:

TP 18

基金资助:

国家重点研发计划项目(No.2020YFC2003502)、国家自然科学基金项目(No.62276038)资助

通讯作者: 张清华,博士,教授,主要研究方向为粗糙集、模糊集、粒计算、三支决策等.E-mail:zhangqh@cqupt.edu.cn.

作者简介: 肖嘉瑜,硕士研究生,主要研究方向为智能信息处理、数据挖掘.E-mail:xiaojiayuhhh@163.com.艾志华,硕士研究生,主要研究方向为智能信息处理、数据挖掘.E-mail:azh15213138308@163.com.王国胤,博士,教授,主要研究方向为粗糙集、粒计算、认知计算、数据挖掘、智能信息处理等.E-mail:wanggy@cqupt.edu.cn.

引用本文:

张清华, 肖嘉瑜, 艾志华, 王国胤. 自适应半径选择的近邻邻域分类器[J]. 模式识别与人工智能, 2022, 35(11): 989-998. ZHANG Qinghua, XIAO Jiayu, AI Zhihua, WANG Guoyin. Near Neighborhood Classifier with Adaptive Radius Selection. Pattern Recognition and Artificial Intelligence, 2022, 35(11): 989-998.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202211004 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2022/V35/I11/989