Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Modeling and recognition of cursive words with hidden Markov models
Pattern Recognition
Decision Rule for Pattern Classification by Integrating Interval Feature Values
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern characteristics of an evolution between two classes
Fuzzy Sets and Systems - Information processing
Rejection Algorithm for Mis-segmented Characters In Multilingual Document Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
A k-order fuzzy OR operator for pattern classification with k -order ambiguity rejection
Fuzzy Sets and Systems
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Possibilistic network-based classifiers: on the reject option and concept drift issues
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A unified view of class-selection with probabilistic classifiers
Pattern Recognition
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Class-selective rejection is an extension of simple rejection. That is, when an input pattern cannot be reliably assigned to one of the N classes in an N-class problem, it is assigned to a subset of classes that are most likely to issue the pattern, instead of simply being rejected. By selecting more classes, the risk of making an error can be reduced, at the price of subsequently having a larger remaining number of classes. The optimality of class-selective rejection is therefore defined as the best tradeoff between error rate and average number of selected classes. Formally, the tradeoff study is embedded in the framework of decision theory. The average expected loss is expressed as a linear combination of error rate and average number of classes. The minimization of the average expected loss, therefore, provides the best tradeoff. The complexity of the resulting optimum rule is reduced, via a discrete convex minimization, to be linear in the number of classes. Upper-bounds on error rate and average number of classes are derived. An example is provided to illustrate various aspects of the optimum decision rule. Finally, the implications of the new decision rule are discussed.