Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The FERET Evaluation Methodology for Face-Recognition Algorithms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An experimental comparison of performance measures for classification
Pattern Recognition Letters
The ROC manifold for classification systems
Pattern Recognition
Asymptotic biometric analysis for large gallery sizes
IEEE Transactions on Information Forensics and Security
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper, we propose a method to predict the presence or absence of correct classification results in classification problems with many classes and the output of the classifier is provided in the form of a ranking list. This problem differs from the ''traditional'' classification tasks encountered in pattern recognition. While the original problem of forming a ranking of the most likely classes can be solved by running several classification methods, the analysis presented here is moved one step further. The main objective is to analyse (classify) the provided rankings (an ordered list of rankings of a fixed length) and decide whether the ''true'' class is present on this list. With this regard, a two-class classification problem is formulated where the underlying feature space is built through a characterization of the ranking lists. Experimental results obtained for synthetic data as well as real world face identification data are presented.