Cost-based feature selection for network model choice

Understand

Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation.

• In practice, the cost of computing informative features also needs to be considered.
• This is particularly important for networks because the computational costs of individual features can span several orders of magnitude.
• We addressed this issue for the network model selection problem using two approaches.