Dual feature-based and example-based explanation methods
Andrei V. Konstantinov Boris V. Kozlov Stanislav R. Kirpichenko Lev V. Utkin
A new approach to the local and global explanation is proposed. It is based
on selecting a convex hull constructed for the finite number of points around
an explained instance. The convex hull allows us to consider a dual
representation of instances in the form of convex combinations of extreme
points of a produced polytope. Instead of perturbing new instances in the
Euclidean feature space, vectors of convex combination coefficients are
uniformly generated from the unit simplex, and they form a new dual dataset. A
dual linear surrogate model is trained on the dual dataset. The explanation
feature importance values are computed by means of simple matrix calculations.
The approach can be regarded as a modification of the well-known model LIME.
The dual representation inherently allows us to get the example-based
explanation. The neural additive model is also considered as a tool for
implementing the example-based explanation approach. Many numerical experiments
with real datasets are performed for studying the approach. The code of
proposed algorithms is available.