Classification Based on the Optimal K-Associated Network

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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