在复杂纺织品制造用例中利用可解释技术进行质量预测
Utilising Explainable Techniques for Quality Prediction in a Complex Textiles Manufacturing Use Case
AI总结：
本文针对复杂纺织品制造数据集，使用Random Forest等树模型与Boruta等特征选择方法进行产品故障分类，并利用可解释技术提取人类可读的规则列表，证明了数据预处理中引入额外特征对模型性能的积极影响。
摘要：
本文开发了一种方法，使用可解释技术对复杂纺织品制造数据集中的产品故障实例进行分类。本研究使用的数据集来自新西兰一家羊毛地毯和小地毯制造商。在研究准确性与可解释性之间的权衡时，评估了三种不同的基于树的分类算法：Decision Tree（决策树）和两种集成方法 Random Forest 和 XGBoost。此外，还评估了三种特征选择方法：使用 chi-squared 作为评分函数的 SelectKBest 方法、Pearson Correlation Coefficient（皮尔逊相关系数）以及 Boruta 算法。不出所料，集成方法通常比 Decision Tree 模型产生更好的结果。Random Forest 模型与 Boruta 特征选择技术结合时总体产生了最佳结果。最后，使用一种树集成解释技术提取规则列表，以捕获经过训练的模型进行分类的必要和充分条件，这些条件人类可以轻松解释。值得注意的是，提取的规则列表中的几个特征是统计特征和计算特征，这些特征被添加到原始数据集中。这表明在数据预处理阶段引入额外信息对最终模型性能的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
