Abstract
The combining approach to classification so-called Multiple Classifier Systems (MCSs) is nowadays one of the most promising directions in pattern recognition and gained a lot of interest through recent years. A large variety of methods that exploit the strengths of individual classifiers have been developed. The most popular methods have their origins in voting, where the decision of a common classifier is a combination of individual classifiers’ outputs, i.e. class numbers or values of discriminants. Of course to improve performance and robustness of compound classifiers, different and diverse individual classifiers should be combined. This work focuses on the problem of fuser design. We present some new results of our research and propose to train a fusion block by algorithms that have their origin in neural computing. As we have shown in previous works, we can produce better results combining classifiers than by using the abstract model of fusion so-called Oracle. The results of our experiments are presented to confirm our previous observations.
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Wozniak, M., Zmyslony, M. (2011). Combining Classifier with a Fuser Implemented as a One Layer Perceptron. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_29
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DOI: https://doi.org/10.1007/978-3-642-20042-7_29
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