Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Training dynamics and neural network performance
Neural Networks
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
To reject or not to reject: that is the question-an answer in caseof neural classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Linear independence of internal representations in multilayer perceptrons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
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Currently, a growing quantity of the Artificial Intelligence tasks demand a high efficiency of the classification systems (classifiers); making an error in the classification of an object or event can cause serious problems. This is worrying when the classifiers confront tasks where the classes are not linearly separable, the classifiers efficiency diminishes considerably. One solution for decreasing this complication is the Rejection Option. In several circumstances it is advantageous to not have a decision be taken and wait to obtain additional information instead of making an error. This work contains the description of a novel reject procedure whose purpose is to identify elements with a high risk of being misclassified; like those in an overlap zone. For this, the location of the object in evaluation is calculated with regard to two hyperplanes that emulate the classifiers decision boundary. The area between these hyperplanes is named an overlap region. If the element is localized in this area, it is rejected. Experiments conducted with the artificial neural network Multilayer Perceptron, trained with the Backpropagation algorithm, show between 12.0%- 91.4%of the objects in question would have been misclassified if they had not been rejected.