Handling missing attribute values in preterm birth data sets

被引：0
|
作者：
Grzymala-Busse, JW [1 ]
Goodwin, LK
Grzymala-Busse, WJ
Zheng, XQ
机构：
[1] Univ Kansas, Dept Elect Engn & Comp Sci, Lawrence, KS 66045 USA
[2] Polish Acad Sci, Inst Comp Sci, PL-01237 Warsaw, Poland
[3] Duke Univ, Nursing Informat Program, Durham, NC 27710 USA
[4] Filterlogix, Lawrence, KS 66049 USA
[5] PC Sprint, Overland Pk, KS 66211 USA
来源：
ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, PT 2, PROCEEDINGS | 2005年 / 3642卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing attribute values, based on most common values and closest fit for symbolic attributes, averages for numerical attributes, and a special approach to induce only certain rules from specified information using the MLEM2 approach. The final conclusion is that the best strategy was to use the global most common method for symbolic attributes and the global average method for numerical attributes.
引用
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页码：342 / 351
页数：10
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