Adaptive and Natural Computing Algorithms

Currently, a growing quantity of the Articial Intelligence tasks demand a high eciency of the classication systems (classiers); making an error in the classication of an object or event can cause serious problems. This is worrying when the classiers confront tasks where the classes are not linearly separable, the classiers eciency 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 misclassied; 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 classiers 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 articial neural network Multilayer Perceptron, trained with the Backpropagation algorithm, show between 12.0%- 91.4%of the objects in question would have been misclassied if they had not been rejected