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Network-based classification (machine learning)
In machine learning, classification is the problem of identifying the sub-population (or class) of each entity based on the observed features of the entity. Network-based classification uses prior knowledge of relationships between the observed features in order to constrain the classification problem. These relationship are given in the form of a network model in which the nodes of the network represent the observed features and the edges represent feature interdependencies.
Problem specification
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