Name
Abstract | ||
|---|---|---|
Outliers are difficult to handle because some of them can be measurement errors, while others may represent phenomena of interest, something “significant” from the viewpoint of the application domain. Statistical methods for managing outliers do not distinguish
between these two possibilities. In our previous work, we suggested a method for distinguishing these two possibilities by
modelling “real measurements” — how measurements should be distributed in a domain of interest. In this paper, we make this
distinction by modelling measurement errors instead. The proposed method is better suited to those applications where it is
difficult to obtain relevant knowledge about real measurements. The test data collected from a recent glaucoma case finding
study in a general practice are used to evaluate the method.
|
Year | DOI | Venue |
|---|---|---|
1997 | 10.1007/BFb0052870 | IDA |
Keywords | Field | DocType |
modelling noisy data,measurement error,data collection | Data mining,Noisy data,False alarm,Computer science,Outlier,General practice,Test data,Application domain,Artificial intelligence,Constant false alarm rate,Machine learning,Observational error | Conference |
Volume | ISSN | ISBN |
1280 | 0302-9743 | 3-540-63346-4 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Xingwang Wu | 1 | 4 | 1.48 |
| Gongxian Cheng | 2 | 5 | 1.86 |
| Xiaohui Liu | 3 | 115 | 18.03 |