Name
Abstract | ||
|---|---|---|
Error Correcting Output Coding is a common technique for multiple class classification tasks which decomposes the original problem in several two-class problems solved through dichotomizers. Such classification system can be improved with a reject option which can be defined according to the level of information available from the dichotomizers. This paper analyzes how this knowledge is useful when applying such reject rules. The nature of the outputs, the kind of the employed classifiers and the knowledge of their loss function are influential details for the improvement of the general performance of the system. Experimental results on popular benchmark data sets are reported to show the behavior of the different schemes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.1055 | Pattern Recognition |
Keywords | Field | DocType |
error correction codes,pattern classification,ECOC reject rules,classification system,dichotomizers,error correcting output coding,multiple class classification tasks,system knowledge,Error Correcting Output Coding,Reject option | Data mining,High-definition video,Data set,Pattern recognition,Computer science,Coding (social sciences),Hamming distance,Artificial intelligence,Decoding methods,Machine learning,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 3 |
PageRank | References | Authors |
0.41 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Simeone | 1 | 28 | 3.56 |
| Claudio Marrocco | 2 | 84 | 17.53 |
| Francesco Tortorella | 3 | 370 | 43.39 |