Abstract
In this paper we propose a design pattern for self-optimizing classification systems, i.e. classifiers able to adapt their behavior to the system changes. First, we provide a formalization of a self-optimizing classifier we use to derive the design pattern. Then, we describe the pattern classes, their interactions, and validate our approach applying the proposed pattern to a real scenario. Finally, to evaluate the proposed solution we compare the behavior of the self-optimizing classifier with a not self-optimizing one. Experimental results demonstrate the approach effectiveness.
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Pasquali, M., Dazzi, P., Panciatici, A., Baraglia, R. (2008). Self-Optimizing Classifiers: Formalization And Design Pattern. In: Priol, T., Vanneschi, M. (eds) From Grids to Service and Pervasive Computing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09455-7_13
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DOI: https://doi.org/10.1007/978-0-387-09455-7_13
Publisher Name: Springer, Boston, MA
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