A budget-constrained inverse classification framework for smooth classifiers
Published in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017
Recommended citation: M.T. Lash, Q. Lin, W.N. Street and J.G. Robinson, A budget-constrained inverse classification framework for smooth classifiers, in Data Mining Workshops (ICDMW), 2017 IEEE International Conference on, pp. 1184-1193, 2017. http://michael-lash.github.io/files/budget_constrained_icdmw2017.pdf
We propose a method and framework for the inverse classification problem that assumes the model in question is differentiable with an L-Lipschitz continuous gradient.
Recommended citation: M.T. Lash, Q. Lin, W.N. Street and J.G. Robinson, A budget-constrained inverse classification framework for smooth classifiers, in Data Mining Workshops (ICDMW), 2017 IEEE International Conference on, pp. 1184-1193, 2017.