Suggestion for Aquaphotomics-Oriented Skin Data Analysis using Explainable Artificial Intelligence: Applications of SHAP, LIME, Lightgbm, ELI5, PDPbox, and Skater for Dataset Categorization and Process Interpretation

In recent years, research has been active in various fields to measure and collect spectrum data on the moisture content of a wide variety of plants and animals, beauty products, concrete, cement, etc., and to clearly display this data using a display method known as an aquagram. In light of this trend, in this thesis study, we propose a method for the automatic classification of aquagrams using various exploitable artificial intelligence (XAI)-based programming techniques. In doing so, we show and explain the process of their classification and the fact that it is possible to show the indicative value of the validity and rationale of the classification, to a certain extent. We have selected XAI based on Explain Like SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light Gradient Boosting Machine (LightGBM), I’m 5 (ELI5), Partial Dependency Plot box (PDPbox), and Skater to analyze diverse datasets, in this study, in particular, aquagram datasets. We intend to thereby present the field with a numerical method to illustrate the seemingly obscure processes and arguments of machine learning, particularly deep learning, classification, which will be useful for future research. Concretely, after investigating the previously obtained matrix-formed aquagram data, we describe the case of explicit classification by machine learning for four different groups of datasets on skin moisture content and moisture transpiration. The programs we use for these are all coded in Python and import and use packages such as pandas, pickle, etc.