Title: Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization,
Abstract: In recent years, a number of artificial intelligent services have been
developed such as defect detection system or diagnosis system for customer
services. Unfortunately, the core in these services is a black-box in which
human cannot understand the underlying decision making logic, even though the
inspection of the logic is crucial before launching a commercial service. Our
goal in this paper is to propose an analytic method of a model explanation that
is applicable to general classification models. To this end, we introduce the
concept of a contribution matrix and an explanation embedding in a constraint
space by using a matrix factorization. We extract a rule-like model explanation
from the contribution matrix with the help of the nonnegative matrix
factorization. To validate our method, the experiment results provide with open
datasets as well as an industry dataset of a LTE network diagnosis and the
results show our method extracts reasonable explanations.