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In recent years, a number of artificial intelligent services have beendeveloped such as defect detection system or diagnosis system for customerservices. Unfortunately, the core in these services is a black-box in whichhuman cannot understand the underlying decision making logic, even though theinspection of the logic is crucial before launching a commercial service. Ourgoal in this paper is to propose an analytic method of a model explanation thatis applicable to general classification models. To this end, we introduce theconcept of a contribution matrix and an explanation embedding in a constraintspace by using a matrix factorization. We extract a rule-like model explanationfrom the contribution matrix with the help of the nonnegative matrixfactorization. To validate our method, the experiment results provide with opendatasets as well as an industry dataset of a LTE network diagnosis and theresults show our method extracts reasonable explanations.

Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization