# Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation

arXiv: https://arxiv.org/abs/2011.13429 · HTML: https://mlsanity.com/paper/2011.13429/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes](https://mlsanity.com/paper/2103.12222/) (2021)
- [Explainable Predictive Modeling for Limited Spectral Data](https://mlsanity.com/paper/2202.04527/) (2022)
- [Unlocking Layer-wise Relevance Propagation for Autoencoders](https://mlsanity.com/paper/2303.11734/) (2023)

## Abstract (arXiv)

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