Abstract
We describe how an ’in house’ classifier can enhance the performance of a commercial ’black box’ classifier using the classic serial multiple classifier combination scheme. It is now acknowledged by the classifier combination community that parallel or hybrid decision fusion algorithms, in general, outperform serial combination schemes. However, classifier combination using techniques that use class labeling, ranking or probability estimators need access to low level information supplied by all of the participating classifiers. Unfortunately, in many commercial applications the classifier is often a’black box’, which implies that it is not possible to manipulate the low level information regarding classification for these classifiers. In many such cases, a serial classifier combination model provides the only practical method to improve classification. In this paper, we present such an application in speech recognition.
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© 2004 Springer-Verlag Berlin Heidelberg
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Rahman, F., Tarnikova, Y., Kumar, A., Alam, H. (2004). Second Guessing a Commercial’Black Box’ Classifier by an’In House’ Classifier: Serial Classifier Combination in a Speech Recognition Application. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_37
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DOI: https://doi.org/10.1007/978-3-540-25966-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22144-9
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