Imputation of incomplete data using adaptive ellipsoids with linear regression

被引：3

作者：

Yao, Leehter ^{[1
]}

Weng, Kuei-Sung ^{[1
]}

机构：

[1] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2015年 / 29卷 / 01期

关键词：

Incomplete data; fuzzy clustering; particle swarm optimization; Gustafson-Kessel algorithm; linear regression; MISSING VALUE ESTIMATION; CLUSTER SUBSTRUCTURE; VALUES; CLASSIFICATION; ALGORITHM;

D O I：

10.3233/IFS-151592

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An efficient scheme for imputing the features missing of incomplete data is proposed in this paper. The missing features are imputed based on a group of nearest complete data in the space of residual features of the incomplete data to be recovered. In order to find the complete data points in the space of residual features, an algorithm called the evolutionary Gustafson-Kessel algorithm (EGKA) is proposed that learns the ellipsoid to adaptively cluster the complete data points with the recovered incomplete data points. A linear regression model is utilized to impute the missing features based on the complete data clustered by the ellipsoid learned by the EGKA.

引用

页码：253 / 265

页数：13

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