A Weighted Fuzzy Rough Nearest Neighbor Classification Algorithm Based on Multiple Interpolation and Similarity Attribute Analysis

2018 IEEE International Conference of Safety Produce Informatization (IICSPI) Pub Date : 2018-12-01 DOI:10.1109/IICSPI.2018.8690500

Chao Xu, Daiwei Li, Haiqing Zhang, Wenfeng Hou, Tianrui Li

{"title":"A Weighted Fuzzy Rough Nearest Neighbor Classification Algorithm Based on Multiple Interpolation and Similarity Attribute Analysis","authors":"Chao Xu, Daiwei Li, Haiqing Zhang, Wenfeng Hou, Tianrui Li","doi":"10.1109/IICSPI.2018.8690500","DOIUrl":null,"url":null,"abstract":"Upper and lower approximation of fuzzy-rough set membership degree is used to solve uncertainty of classification problem in FRNN (Fuzzy Rough Nearest Neighbor) algorithm. Although FRNN is the current leading classification algorithm, misjudgments still tend to occur when handling similar attribute values. Combining multiple interpolation algorithms and similarity attribute analysis, this paper proposes a new classification algorithm, which is called weighted Fuzzy Rough Nearest Neighbor (WFRNN) classification algorithm. WFRNN adds the corresponding weight of each attribute for the sample, and then multiple interpolations are used to fill data sets and the other four kinds of packing method are adopted to fill the missing data set. Then five completely random missing data sets from UCI were used in comparison experiments. We have compared WFRNN with classic KNN, decision tree, FRNN, J48, and random forests. Experimental performances show that the WFRNN algorithm can predict more accuracy classification results.","PeriodicalId":6673,"journal":{"name":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICSPI.2018.8690500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Upper and lower approximation of fuzzy-rough set membership degree is used to solve uncertainty of classification problem in FRNN (Fuzzy Rough Nearest Neighbor) algorithm. Although FRNN is the current leading classification algorithm, misjudgments still tend to occur when handling similar attribute values. Combining multiple interpolation algorithms and similarity attribute analysis, this paper proposes a new classification algorithm, which is called weighted Fuzzy Rough Nearest Neighbor (WFRNN) classification algorithm. WFRNN adds the corresponding weight of each attribute for the sample, and then multiple interpolations are used to fill data sets and the other four kinds of packing method are adopted to fill the missing data set. Then five completely random missing data sets from UCI were used in comparison experiments. We have compared WFRNN with classic KNN, decision tree, FRNN, J48, and random forests. Experimental performances show that the WFRNN algorithm can predict more accuracy classification results.

查看原文本刊更多论文

基于多次插值和相似属性分析的加权模糊粗糙近邻分类算法

采用模糊粗糙集隶属度的上下近似来解决模糊粗糙近邻算法中分类问题的不确定性。虽然FRNN是目前领先的分类算法，但在处理相似属性值时仍然容易出现误判。结合多种插值算法和相似属性分析，提出了一种新的分类算法，即加权模糊粗糙近邻(WFRNN)分类算法。WFRNN为样本添加每个属性对应的权值，然后使用多次插值填充数据集，另外四种填充方法填充缺失的数据集。然后利用UCI的5个完全随机缺失数据集进行对比实验。我们将WFRNN与经典的KNN、决策树、FRNN、J48和随机森林进行了比较。实验结果表明，WFRNN算法可以预测更准确的分类结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2018 IEEE International Conference of Safety Produce Informatization (IICSPI)

自引率

0.00%

发文量