Abstract
In this paper, we propose a new graph-based classifier which uses a special network, referred to as optimal K-associated network, for modeling data. The K-associated network is capable of representing (dis)similarity relationships among data samples and data classes. Here, we describe the main properties of the K-associated network as well as the classification algorithm based on it. Experimental evaluation indicates that the model based on an optimal K-associated network captures topological structure of the training data leading to good results on the classification task particularly for noisy data.
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Lopes, A.A., Bertini, J.R., Motta, R., Zhao, L. (2009). Classification Based on the Optimal K-Associated Network. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_117
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DOI: https://doi.org/10.1007/978-3-642-02466-5_117
Publisher Name: Springer, Berlin, Heidelberg
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