Improving the Quality of Explanations with Local Embedding Perturbations
KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Anchorage AK USA August, 2019(2019)
摘要
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 dimensionality, then generates neighbors in the local embedding and projects them back to the original space. Experimental results show that our method generates more realistic neighborhoods and consequently better explanations. It can be used in combination with existing local explanation algorithms.
更多查看译文
关键词
black-box explanations, explainability, interpretability, local explanations, local intrinsic dimensionality
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络