Improving the quality of explanations with local embedding perturbations

Abstract

Classifier explanations have been identified as a crucial component of knowledge discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given instance. A key step in this approach is to generate synthetic neighbors of the given instance. This neighbor generation process is challenging and it has considerable impact on the quality of explanations. To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. Based on this, we then propose a new neighborhood generation method. Our method first fits a local embedding/subspace around a given instance using the LID of the test instance as the target dim..