1115 search results - page 3 / 223 » On classification with missing data using rough-neuro-fuzzy ... |
NIPS
1994
13 years 9 months ago
1994
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
RSCTC
2000
Springer
13 years 11 months ago
2000
Springer
: In the paper nine different approaches to missing attribute values are presented and compared. Ten input data files were used to investigate the performance of the nine methods t...
SIGMOD
2008
ACM
14 years 7 months ago
2008
ACM
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
JSS
2008
13 years 7 months ago
2008
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can...
BMCBI
2007
13 years 7 months ago
2007
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...