On optimal reject rules and ROC curves
Pattern Recognition Letters
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Confidence-based classifier design
Pattern Recognition
A mathematical framework to optimize ATR systems with non-declarations and sensor fusion
Computers and Operations Research
Reliability estimation of a statistical classifier
Pattern Recognition Letters
Computers and Electronics in Agriculture
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
Growing a multi-class classifier with a reject option
Pattern Recognition Letters
A k-order fuzzy OR operator for pattern classification with k -order ambiguity rejection
Fuzzy Sets and Systems
Tunnel Hunter: Detecting application-layer tunnels with statistical fingerprinting
Computer Networks: The International Journal of Computer and Telecommunications Networking
Minimum spanning tree based one-class classifier
Neurocomputing
The use of features selection and nearest neighbors rule for faults diagnostic in induction motors
Engineering Applications of Artificial Intelligence
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
Adaptive classification with jumping emerging patterns
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Computational Statistics & Data Analysis
A family of measures for best top-n class-selective decision rules
Pattern Recognition
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Directed enumeration method in image recognition
Pattern Recognition
Design of reject rules for ECOC classification systems
Pattern Recognition
The asymptotic distribution of an estimator of the Bayes error rate
Pattern Recognition Letters
A multiclass, k-NN approach to Bayes risk estimation
Pattern Recognition Letters
A distance based classification method using an incomplete training set
Pattern Recognition Letters
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Object detection in video using Lorenz information measure and discrete wavelet transform
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Multiple classifier combination using reject options and markov fusion networks
Proceedings of the 14th ACM international conference on Multimodal interaction
Multi-label classification with a reject option
Pattern Recognition
Serial fusion of random subspace ensemble for subcellular phenotype images classification
International Journal of Bioinformatics Research and Applications
An introduction to artificial prediction markets for classification
The Journal of Machine Learning Research
Neural Computation
A unified view of class-selection with probabilistic classifiers
Pattern Recognition
Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles
Machine Vision and Applications
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
Review: A review of novelty detection
Signal Processing
Random subspace support vector machine ensemble for reliable face recognition
International Journal of Biometrics
The data replication method for the classification with reject option
AI Communications
Alert correlation: Severe attack prediction and controlling false alarm rate tradeoffs
Intelligent Data Analysis
Hi-index | 754.84 |
The performance of a pattern recognition system is characterized by its error and reject tradeoff. This paper describes an optimum rejection rule and presents a general relation between the error and reject probabilities and some simple properties of the tradeoff in the optimum recognition system. The error rate can be directly evaluated from the reject function. Some practical implications of the results are discussed. Examples in normal distributions and uniform distributions are given.