Abstract
In this paper, a Single Classifier-based Multiple Classification Scheme (SMCS) is proposed as an alternative multiple classification scheme. The SMCS uses only a single classifier to generate multiple classifications for a given test data point. Because of the presence of multiple classifications, classification combination schemes, such as majority voting, can be applied, and so the mechanism may improve the recognition rate in a manner similar to that of Multiple Classifier Systems (MCS). The experimental results confirm the validity of the proposed SMCS as applicable to many classification systems.
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Ko, A.HR., Sabourin, R. (2013). Single Classifier Based Multiple Classifications. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_12
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DOI: https://doi.org/10.1007/978-3-642-38067-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
Online ISBN: 978-3-642-38067-9
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