Name
Abstract | ||
|---|---|---|
This paper presents a framework for the analysis of similarity among abstract-level classifiers and proposes a methodology for the evaluation of combination methods. In this paper, each abstract-level classifier is considered as a random variable, and sets of classifiers with different degrees of similarity are systematically simulated, combined, and studied. It is shown to what extent the performance of each combination method depends on the degree of similarity among classifiers and the conditions under which each combination method outperforms the others. Experimental tests have been carried out on simulated and real data sets. The results confirm the validity of the proposed methodology for the analysis of combination methods and its usefulness for multiclassifier system design. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/s10032-002-0099-z | IJDAR |
Keywords | Field | DocType |
Abstract-level classifiers,Classification,Combination methods,Multiclassifier systems,Similarity | Similitude,Random variable,Data set,Degree of similarity,Pattern recognition,Random subspace method,Computer science,Systems design,Artificial intelligence,Classifier (linguistics),Machine learning | Journal |
Volume | Issue | Citations |
6 | 1 | 5 |
PageRank | References | Authors |
0.57 | 17 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Bovino | 1 | 18 | 1.59 |
| Giovanni Dimauro | 2 | 190 | 24.09 |
| Sebastiano Impedovo | 3 | 217 | 26.47 |
| M. G. Lucchese | 4 | 67 | 4.23 |
| R. Modugno | 5 | 56 | 4.02 |
| G. Pirlo | 6 | 552 | 39.16 |
| A. Salzo | 7 | 157 | 14.06 |
| L. Sarcinella | 8 | 83 | 5.97 |