18 research outputs found
A rejection option for the multilayer perceptron using hyperplanes
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