Name
Abstract | ||
|---|---|---|
Supervised classifiers are usually based on a set of predictors given in the learning sample as well as in later test samples. Especially in the medical field a reduction of the number of examinations is often desired to save patients time and costs. The approach of indirect classification makes use of all available variables of the learning sample, although it classifies based only on a reduced set of variables. A general definition of indirect classification is given and a specific generalised indirect classifier is proposed. This classifier combines an arbitrary number of regression models which predict those variables that are not acquired for future observations. The performance of the generalised indirect classifier is investigated by using a simulation model which mimics different kinds of decision surfaces and by the application to different data sets. Misclassification results of direct and indirect classifiers are compared. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.csda.2004.06.008 | Computational Statistics & Data Analysis |
Keywords | DocType | Volume |
combining predictive models,different data set,supervised classifier,arbitrary number,later test sample,indirect classifier,supervised classification,generalised indirect classifier,different kind,misclassification result,indirect classification,specific generalised indirect classifier,simulation model,regression model,prediction model | Journal | 49 |
Issue | ISSN | Citations |
3 | Computational Statistics and Data Analysis | 1 |
PageRank | References | Authors |
0.41 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Peters | 1 | 1 | 0.41 |
| T. Hothorn | 2 | 58 | 5.43 |
| B. Lausen | 3 | 58 | 5.29 |