2020/11/26 by Ihsan Ullah, hsan Ullah, Ullah, hsan +8 · 8 citations
Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Deep learning #Explainable Artificial Intelligence (XAI) #Layer (electronics) #Materials science #Nanotechnology #Political science #Relevance (law) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2011.13429
published in arXiv (Cornell University) (Cornell University) · 13 pages, 5 figures, 6 tables
arxiv created 2020/11/26 · openalex publication_date 2020/11/26 · arxiv updated 2020/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Trust and credibility in machine learning models is bolstered by the ability of a model to explain itsdecisions. While explainability of deep learning models is a well-known challenge, a further chal-lenge is clarity of the explanation itself, which must be interpreted by downstream users. Layer-wiseRelevance Propagation (LRP), an established explainability technique developed for deep models incomputer vision, provides intuitive human-readable heat maps of input images. We present the novelapplication of LRP for the first time with structured datasets using a deep neural network (1D-CNN),for Credit Card Fraud detection and Telecom Customer Churn prediction datasets. We show how LRPis more effective than traditional explainability concepts of Local Interpretable Model-agnostic Ex-planations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectivenessis both local to a sample level and holistic over the whole testing set. We also discuss the significantcomputational time advantage of LRP (1-2s) over LIME (22s) and SHAP (108s), and thus its poten-tial for real time application scenarios. In addition, our validation of LRP has highlighted features forenhancing model performance, thus opening up a new area of research of using XAI as an approachfor feature subset selection