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基于近邻噪声处理的KNN缺失数据填补算法
Alternative TitlePredicting Missing Values with KNN Based on the Elimination of Neighbor Noise
郝胜轩; 宋宏; 周晓锋
Department数字工厂研究室
Source Publication计算机仿真
ISSN1006-9348
2014
Volume31Issue:7Pages:264-268
Indexed ByCSCD
CSCD IDCSCD:5206116
Contribution Rank1
Funding Organization北京市自然科学基金(7110001)
Keyword缺失数据填补 近邻 噪声最近邻
Abstract在优化算法的研究中,针对KNN算法对缺失数据的填补效果会因为原始数据中存在噪声而受到严重影响的问题,根据待填补缺失数据最近邻的近邻关系,提出了一种新的缺失数据填补算法——ENN-KNN(Eliminate Neighbor Noise k-Nearest Neighbor)。通过比较待填补缺失数据每个最近邻的真实近邻程度能够有效地识别潜在的噪声最近邻。最后使用所有非噪声最近邻对待填补缺失数据进行填补,从而消除了噪声最近邻对填补结果的影响。通过观察四组UCI数据集的仿真结果,可知ENN-KNN算法的填补准确性总体上要优于KNN算法。
Other AbstractTraditional KNN imputation method for dealing with missing data is severely affected by the noise in the original data. This paper presents a novel imputation method for dealing with missing data,which is based on the relationship of nearest neighbors of missing data-ENN-KNN( Eliminate Neighbor Noise k - Nearest Neighbor) . ENN - KNN imputation method can effectively identify potential noise nearest neighbor by comparing each real nearest degree of nearest neighbor of missing data. It uses all nearest neighbors which are not noise nearest neighbor to deal with missing data,for this reason it can eliminate the effect of noise nearest neighbor for dealing with missing data. The experiment results of four groups of UCI data sets show that the ENN - KNN imputation method is overall superior to KNN imputation method on the performance of prediction accuracy.
Language中文
Citation statistics
Cited Times:3[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15198
Collection数字工厂研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
郝胜轩,宋宏,周晓锋. 基于近邻噪声处理的KNN缺失数据填补算法[J]. 计算机仿真,2014,31(7):264-268.
APA 郝胜轩,宋宏,&周晓锋.(2014).基于近邻噪声处理的KNN缺失数据填补算法.计算机仿真,31(7),264-268.
MLA 郝胜轩,et al."基于近邻噪声处理的KNN缺失数据填补算法".计算机仿真 31.7(2014):264-268.
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