Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDefect DetectionGeneral ClassificationSimilar Papers 제목 키워드 기반
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classi…
Decision MakingDeep LearningPatent classificationEntropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods …
Explainable artificial intelligenceImage ClassificationAutomatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators
Automatic legal text classification systems have been proposed in the literature to address knowledge extraction from judgments and detect their aspects. However, most of these systems are black boxes even when their mod…
Explainable artificial intelligencetext-classificationText ClassificationLogic Explained Networks
The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their deci…
Explainable artificial intelligenceTowards Human-Understandable Visual Explanations: Human Imperceptible Cues Can Better Be Removed
Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction. In this paper, we call them "distinguishing f…
Explainable Artificial Intelligence (XAI)