New Method Based on Rough Set for Filling Missing Value

被引：1

作者：

Cekik, R. ^{[1
]}

Telceken, S. ^{[2
]}

机构：

[1] Anadolu Univ, Dept Comp Engn, TR-26555 Eskisehir, Turkey

[2] Anadolu Univ, Dept Comp Sci, TR-26555 Eskisehir, Turkey

来源：

RECENT DEVELOPMENTS AND THE NEW DIRECTION IN SOFT-COMPUTING FOUNDATIONS AND APPLICATIONS | 2018年 / 361卷

关键词：

Missing value; Rough set; Data mining;

D O I：

10.1007/978-3-319-75408-6_4

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The presence of missing value in a dataset can affect the performance of an analysis system such as classifier. To solve this problem many methods have been proposed in different studies using different theorems, analysis systems and methods such as Neural Network (NN), k-Nearest Neighbor (k-NN), closest fit etc. In this paper, we propose novel method based on RST for solving the problem of missing value that was lost (e.g., was erased). After dataset filling with proposed method, it has been observed improvement the performance of used analysis systems.

引用

页码：41 / 48

页数：8

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