Detection and Analysis of Hair
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concave Learners for Rankboost
The Journal of Machine Learning Research
Probabilistic model supported rank aggregation for the semantic concept detection in video
Proceedings of the 6th ACM international conference on Image and video retrieval
Using multi-instance enrollment to improve performance of 3D face recognition
Computer Vision and Image Understanding
An experimental comparison of performance measures for classification
Pattern Recognition Letters
The Use of Fuzzy t-Conorm Integral for Combining Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Fusion in Multibiometric Identification Systems: What about the Missing Data?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Parallel versus Serial Classifier Combination for Multibiometric Hand-Based Identification
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
News video retrieval by learning multimodal semantic information
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
A probability model for combining ranks
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Rank-Based decision fusion for 3d shape-based face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
AP-based borda voting method for feature extraction in TRECVID-2004
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
| Hi-index | 0.14 |
Classifier combination holds the potential of improving performance by combining the results of multiple classifiers. 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. 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.