Name
Abstract | ||
|---|---|---|
A well established technique to improve the classification performances is to combine more classifiers. In the binary case, an effective instrument to analyze the dichotomizers under different class and cost distributions providing a description of their performances at different operating points is the Receiver Operating Characteristic (ROC) curve. To generate a ROC curve, the outputs of the dichotomizers have to be processed. An alternative way that makes this analysis more tractable with mathematical tools is to use a parametric model and, in particular, the binormal model that gives a good approximation to many empirical ROC curves. Starting from this model, we propose a method to estimate the ROC curve of the linear combination of two dichotomizers given the ROC curves of the single classifiers. A possible application of this approach has been successfully tested on real data set. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11553595_95 | ICIAP |
Keywords | Field | DocType |
linearly combined dichotomizers,different operating point,binormal model,parametric model,empirical roc curve,roc curve,cost distribution,different class,binary case,receiver operating characteristic,classification performance,receiver operator characteristic | Linear combination,Receiver operating characteristic,Parametric model,Pattern recognition,Computer science,Support vector machine,Multilayer perceptron,Cumulative distribution function,Artificial intelligence,True positive rate,Binary number | Conference |
Volume | ISSN | ISBN |
3617 | 0302-9743 | 3-540-28869-4 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Marrocco | 1 | 84 | 17.53 |
| Mario Molinara | 2 | 91 | 18.19 |
| Francesco Tortorella | 3 | 370 | 43.39 |