Name
Abstract | ||
|---|---|---|
Classifier combination holds the potential of improving performance by combining the results of multiple classifers. For domains with very large numbers of classes, such as biometrics, we present an axiomatic framework of desirable mathematical properties for combination functions of rank-based classifiers. This framework represents a continuum of combination rules, including the Borda Count, Logistic Regression, and Highest Rank combination methods as extreme cases [11], [23], [4], [13]. Intuitively, this framework captures how the two complementary concepts of general preference for specific classifiers and the confidence it has in any specific result (as indicated by ranks) can be balanced while maintaining consistent rank interpretation. Mixed Group Ranks (MGR) is a new combination function that balances preference and confidence by generalizing these other functions. We demonstrate that MGR is an effective combination approach by performing multiple experiments on data sets with large numbers of classes and classifiers from the FERET face recognition study. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TPAMI.2004.48 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
effective combination approach,general preference,mixed group rank,combination rule,new combination function,combination function,framework capture,large number,axiomatic framework,highest rank combination method,classifier combination,classification,sensor fusion,biometrics,learning artificial intelligence,face recognition,logistic regression,ensemble methods,convex programming | Facial recognition system,Borda count,Pattern recognition,Generalization,Axiom,Artificial intelligence,Biometrics,Classifier (linguistics),Ensemble learning,Convex optimization,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 8 | 0162-8828 |
Citations | PageRank | References |
34 | 2.23 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ofer Melnik | 1 | 55 | 5.91 |
| Yehuda Vardi | 2 | 34 | 2.23 |
| Cun-Hui Zhang | 3 | 174 | 18.38 |