Abstract
In this paper we propose to face the rejection problem as a new classification problem. In order to do that, we introduce a trainable classifier, that we call reject classifier, to distinguish it from the classifier to which the reject option is applied (termed primary classifier). This idea yields a reject option that is largely independent of the approach used for the primary classifier, working also for systems providing as their only output the guess class.
The whole classification system can be seen as a serial multiple classifier system: given an input patter x, the primary classifier limits to two the number of possible classes (i.e., its guess class and the reject class), while the reject classifier attributes x to one out of these two classes.
The proposed reject method has been tested on three different publicly available databases. We also compared it with other reject rules and the results demonstrated the effectiveness of the proposed approach.
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Foggia, P., Percannella, G., Sansone, C., Vento, M. (2007). On Rejecting Unreliably Classified Patterns. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_29
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DOI: https://doi.org/10.1007/978-3-540-72523-7_29
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