Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method

Authors:
Roberto Tronci;Giorgio Giacinto;Fabio Roli
Affiliations:
DIEE Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;DIEE Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123;DIEE Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy 09123
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

Citing 12
Cited 1

On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics

Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Fusion of multiple fingerprint matchers by single-layer perceptron with class-separation loss function

Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Handbook of Multibiometrics (International Series on Biometrics)

Handbook of Multibiometrics (International Series on Biometrics)
An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Dynamic Score Selection for Fusion of Multiple Biometric Matchers

ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Likelihood Ratio-Based Biometric Score Fusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximizing the area under the ROC curve by pairwise feature combination

Pattern Recognition
Selection of Experts for the Design of Multiple Biometric Systems

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition

Dynamic linear combination of two-class classifiers

SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

In two-class score-based problems the combination of scores from an ensemble of experts is generally used to obtain distributions for positive and negative patterns that exhibit a larger degree of separation than those of the scores to be combined. Typically, combination is carried out by a "static" linear combination of scores, where the weights are computed by maximising a performance function. These weights are equal for all the patterns, as they are assigned to each of the expert to be combined. In this paper we propose a "dynamic" formulation where the weights are computed individually for each pattern. Reported results on a biometric dataset show the effectiveness of the proposed combination methodology with respect to "static" linear combinations and trained combination rules.