  With the advent of Industry 4.0, Data Science and Explainable Artificial
Intelligence (XAI) has received considerable intrest in recent literature.
However, the entry threshold into XAI, in terms of computer coding and the
requisite mathematical apparatus, is really high. For fault diagnosis of steel
plates, this work reports on a methodology of incorporating XAI based insights
into the Data Science process of development of high precision classifier.
Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids,
insights from XAI tools viz. Ceteris Peribus profiles, Partial Dependence and
Breakdown profiles have been harvested. Additionally, insights in the form of
IF-THEN rules have also been extracted from an optimized Random Forest and
Association Rule Mining. Incorporating all the insights into a single ensemble
classifier, a 10 fold cross validated performance of 94% has been achieved. In
sum total, this work makes three main contributions viz.: methodology based
upon utilization of medoids and SMOTE, of gleaning insights and incorporating
into model development process. Secondly the insights themselves are
contribution, as they benefit the human experts of steel manufacturing
industry, and thirdly a high precision fault diagnosis classifier has been
developed.
