- author = "Chan Gyu Lee and Sungbum Jun",
- title = "Feature Extraction With Genetic Programming for Root Cause Identification in Manufacturing With Interpretable Machine Learning",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2025",
- volume = "29",
- number = "4",
- pages = "1029--1040",
- month = aug,
- keywords = "genetic algorithms, genetic programming, Feature extraction, Manufacturing, Closed box, Fault diagnosis, Data models, Predictive models, Decision trees, decision tree, dimensional awareness, feature extraction, interpretability, machine learning",
- ISSN = "1089-778X",
-
DOI = "
10.1109/TEVC.2024.3388725",
- abstract = "... root cause identification (RCI) is becoming more difficult due to their black-box structures and the tradeoff between accuracy and interpretability. In order to improve performance while maintaining interpretability, we propose a new framework named Feature Extraction for finding Root causes for Manufacturing Applications with Tree-based algorithms (FERMAT), which enhances the performance of height-limited decision trees (DTs) (C4.5) through dimensionally aware genetic programming for feature extraction. Especially in FERMAT, only interpretable features are extracted to prevent DTs from delivering uninterpretable expressions to practitioners. In the present study, FERMAT’s applicability to RCI was verified with both manufacturing and nonmanufacturing datasets with different imbalance ratios. The experimental results showed that FERMAT outperformed the other single-tree-based models by extracting good features and delivered performance comparable to the black-box models.",
-
notes = "also known as \cite{10499858}
Department of Industrial and Systems Engineering, Dongguk University, Seoul, Republic of Korea",
