Machine Learning
Supervised classification with structured class definitions
Computational Statistics & Data Analysis
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Coaching variables for regression and classification
Statistics and Computing
Latent class models for classification
Computational Statistics & Data Analysis
On the exact distribution of maximally selected rank statistics
Computational Statistics & Data Analysis
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
Bootstrap estimated true and false positive rates and ROC curve
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
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.