# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 2 Related Work
**Summary:** XAI is a highly active area of research in the machine learning domain, with a variety of general and model/data specific approaches in the field and continuing to emerge. We have discussed the most relevant explainability approaches related to deep learning models processing images, time-series/financial data, and text input. We note the lack of deep learning XAI approaches applied to structured tabular data. Structured, tabular data is very common in organisations, tending to be an earlier focus for the adoption of machine learning models than unstructured data such as images or text. Explainability of structured data models has been largely limited to those based on traditional machine learning models (with algorithms such as random forest, XGBoost, etc.) using model agnostic techniques such as LIME and SHAP. Organisations want to utilise such data for training a DL network, provided such DL models can be explained. We focus principally on LRP, an established XAI technique for DL models that is typically used for images but can be utilised with modifications for other forms of inputs, providing intuitive visual explanations in the form of heatmaps. It has a number of distinct advantages: It provides intuitive visual explanation highlighting relevant input, produces results quickly, and has not been tried with 1D CNN over structured data. By visually highlighting high influence parts of the input, it should in theory highlight the important features (input) that contribute most to a model decision e.g., customer churn, credit card fraud detection, and loan or insurance rejection. 1D CNN is never or rarely (not in our knowledge to date) used for structured data but we suggest that it can be, with the sliding kernel approach, learning a combination of features in the table that as a group contribute to model decisions. Our motivation for using the traditionally image focused approach of 1D-CNN for tabular data was as follows: Firstly, structured data has a large overlap with image input. It is essentially a matrix of numbers, just as an image is a matrix of pixel values numbers. Pixel values have a fixed range, and this can be achieved in structure data using normalisation. In the case of structured data as input to a 1D-CNN, the positions and combinations of numbers has relevance and are in a fixed set of positions (features). Furthermore, although we do not know whether certain features have correlation or dependencies with each other, CNN will learn that patterns/dependencies/uniqueness that drive to particular classifications, individually, or in combination to other features by identifying the occurrence of feature values. Secondly, in traditional machine learning, features are typically selected manually in the feature extraction stage or by using techniques like LBP [50], SIFT [51], etc., and than in some cases a features subset selection technique is used to improve the model results. This can be a lengthy iterative process e.g., manual subset selection of features or by using a wrapper feature selection method, with iterative model training to seek out redundant low contribution features. By using 1D-CNN with LRP for explanation, the influential features are highlighted as a by-product of the initial model creation exercise. Our focus is on using and enhancing existing XAI techniques for structured data. In addition to use a 1D-CNN model over structured data with LRP for model explanation, we wish to compare the correctness of LRP against leading explainability methods SHAP and LIME in terms of their similarity in selecting important features and time complexity. In the next section, we will discuss the proposed approach.