Pattern Recognition with Rejection - Combining Standard Classification Methods with Geometrical Rejecting

The motivation of our study is to provide algorithmic appro-aches to distinguish proper patterns, from garbage and erroneous patterns in a pattern recognition problem. The design assumption is to provide methods based on proper patterns only. In this way the approach that we propose is truly versatile and it can be adapted to any pattern recognition problem in an uncertain environment, where garbage patterns may appear. The proposed attempt to recognition with rejection combines known classifiers with geometric methods used for separating native patterns from foreign ones. Empirical verification has been conducted on datasets of handwritten digits classification (native patterns) and handwritten letters of Latin alphabet (foreign patterns).

[1]  Horst Bunke,et al.  Rejection strategies for offline handwritten text line recognition , 2006, Pattern Recognit. Lett..

[2]  Witold Pedrycz,et al.  Rejecting Foreign Elements in Pattern Recognition Problem , 2015, ICAART 2015.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  David Casasent,et al.  A support vector hierarchical method for multi-class classification and rejection , 2009, 2009 International Joint Conference on Neural Networks.

[5]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[6]  Ian H. Witten,et al.  One-Class Classification by Combining Density and Class Probability Estimation , 2008, ECML/PKDD.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Robert P. W. Duin,et al.  Growing a multi-class classifier with a reject option , 2008, Pattern Recognit. Lett..

[9]  Thomas Burger,et al.  Dempster-Shafer Based Rejection Strategy for Handwritten Word Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[10]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[11]  Michael Elad,et al.  Pattern detection using a maximal rejection classifier , 2000, 21st IEEE Convention of the Electrical and Electronic Engineers in Israel. Proceedings (Cat. No.00EX377).

[12]  Erik J. Scheme,et al.  Confidence-Based Rejection for Improved Pattern Recognition Myoelectric Control , 2013, IEEE Transactions on Biomedical Engineering.