Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burden, R.L., Douglas Faires, J., Reynolds, A.C.: Numerical Analysis. In: Prindle, Weber, Schmidt (eds.) Wadsworth International (1981)
Cordella, L.P., Stefano, C.D., Tortorella, F., Vento, M.: A Method for Improving Classification Reliability of Multilayer Perceptrons. IEEE Trans. on neural Networks 6(5), 1140–1147 (1995)
Irie, B., Miyake, S.: Capabilities of three-layered perceptrons. In: Proc. of the IEEE Conference on Neural Networks, vol. I, pp. 641–648 (1988)
Lippmann, R.P.: An introduction to computing with neural nets. IEEE Acoust. Speech Signal Process. Mag. 4, 4–22 (1987)
Lin, C.-C., El-Jaroudi, A.: An algorithm to determine the feasibilities and weights of two-layer perceptrons for partitioning and classification. Pattern Recognition 31(11), 1613–1625 (1998)
Looney, C.G.: Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists. Oxford University Press, Oxford (1997)
Makhoul, J., El-Jaroudi, A., Schwartz, R.: Partitioning capabilities of two-layer neural networks. IEEE Trans. Signal Process. 39(6), 436–1440 (1991)
Merz, C.J., Murphy, P.M.: Repository of Machine Learning Databases, University of California at Irvine (1998), http://www.csi.uci.edu/mlearn
Reister, D.B.: The least Squares Fit of a Hyperplane to Uncertain Data. ORNL (1996)
Shah, J.V., Poon, C.-S.: Linear independence of internal representations in multilayer perceptrons. IEEE Trans. on Neural Networks 10(1), 10–18 (1999)
Singh, S., Markou, M.: An Approach to Novelty Detection Applied to the Classification of Images Regions. IEEE Trans. on Knowledge and Data Engineering 16(4), 396–407 (2004)
De Stefano, C., Sansone, C., Vento, M.: To Reject or Not to Reject: That is the Question an Answer in Case of Neural Classifiers. IEEE Transactions on Systems, Man, and Cybernetics Part C Applications end Reviews 30(1), 84–94 (2000)
Tsujitani, M., Koshimizu, T.: Neural Discriminant Analysis. IEEE Transactions on Neural Networks 11(6), 1394–1400 (2000)
Wilson, C.L., Blue, J.L., Omidvar, O.M.: Training dynamics and neural network performance. Neural Networks 10(5), 907–923 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gasca A., E. et al. (2011). A Rejection Option for the Multilayer Perceptron Using Hyperplanes. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-20282-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
Online ISBN: 978-3-642-20282-7
eBook Packages: Computer ScienceComputer Science (R0)