Utilising Explainable Techniques for Quality Prediction in a Complex Textiles Manufacturing Use Case

TL;DR

This study evaluates explainable tree-based models for predicting product failure in textiles manufacturing, highlighting the impact of feature selection and interpretability techniques on model performance.

Contribution

It introduces an approach combining ensemble classifiers with feature selection and explainability methods tailored for complex textiles manufacturing data.

Findings

Ensemble methods outperform single decision trees.

Boruta feature selection yields the best model performance.

Explainable rule extraction aids human understanding of failure conditions.

Abstract

This paper develops an approach to classify instances of product failure in a complex textiles manufacturing dataset using explainable techniques. The dataset used in this study was obtained from a New Zealand manufacturer of woollen carpets and rugs. In investigating the trade-off between accuracy and explainability, three different tree-based classification algorithms were evaluated: a Decision Tree and two ensemble methods, Random Forest and XGBoost. Additionally, three feature selection methods were also evaluated: the SelectKBest method, using chi-squared as the scoring function, the Pearson Correlation Coefficient, and the Boruta algorithm. Not surprisingly, the ensemble methods typically produced better results than the Decision Tree model. The Random Forest model yielded the best results overall when combined with the Boruta feature selection technique. Finally, a tree ensemble explaining technique was used to extract rule lists to capture necessary and sufficient conditions for classification by a trained model that could be easily interpreted by a human. Notably, several features that were in the extracted rule lists were statistical features and calculated features that were added to the original dataset. This demonstrates the influence that bringing in additional information during the data preprocessing stages can have on the ultimate model performance.