基于属性加权的ML-KNN方法  

The ML-KNN method based on attribute weighting

作　　者：温欣 李德玉[1,2] Xin WEN;Deyu LI(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,Shanxi,China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006,Shanxi,China)

机构地区：[1]山西大学计算机与信息技术学院,山西太原030006 [2]山西大学计算智能与中文信息处理教育部重点实验室,山西太原030006

出　　处：《山东大学学报（理学版）》2024年第3期107-117,共11页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(62072294)。

摘　　要：提出了一种基于属性加权的ML-KNN方法。首先使用变精度邻域粗糙集识别来自每一个标记的决策类非正域中的样本,并构造异质样本对;然后基于属性对异质样本对的区分能力评估不同属性对于分类的重要度;最后计算样本之间的加权距离获得其近邻分布,且基于最大化后验概率的原则实现多标记分类。在10个公开的多标记数据集上的实验结果验证了所提方法的有效性。A ML-KNN method based on attribute weighting has been proposed.To be specific,we first identify samples from the non-positive regions of decision classes by means of the variable precision neighborhood rough set model with respect to each label and construct the heterogeneous sample pairs.Then,the significance of different attributes for classification is evaluated based on their discernibility for the heterogeneous sample pairs.Finally,the weighted distances between samples are calculated in order to obtain the nearest neighbor distributions of samples.At the same time,based on the principle of maximizing the posterior probability,the multi-label classification is implemented.Further,the experimental results on ten public multi-label data sets verify the effectiveness of the proposed method.

关 键 词：多标记分类 属性重要度 邻域粗糙集 分类不确定性 异质样本对 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]