Name
Abstract | ||
|---|---|---|
Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s00521-009-0295-6 | Neural Computing and Applications |
Keywords | DocType | Volume |
biometric recognition,pattern recognition technique,missing data problem,pattern classification,missing value,statistical learning theory,real-life classification task,pattern classificationmissing data � neural networksmachine learning,pattern classification task,document classification,problem domain | Journal | 19 |
Issue | ISSN | Citations |
2 | 1433-3058 | 63 |
PageRank | References | Authors |
2.18 | 35 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro J. García-Laencina | 1 | 275 | 14.14 |
| José-Luis Sancho-Gómez | 2 | 182 | 17.26 |
| Aníbal R. Figueiras-Vidal | 3 | 467 | 38.03 |