Paper Number
ECIS2026-1995
Paper Type
CRP
Abstract
The traditional data-driven feature engineering approach often overlooks essential factors that are not explicitly present in the data and can only be identified with the help of domain knowledge. Such approaches often lead to irrelevant or poorly defined features, which in turn hinder model performance and interpretability. Existing research often employs a “theory-driven feature engineering” approach to incorporate domain knowledge, leveraging domain theories to design features for machine learning models. We reviewed the literature on theory-driven feature engineering while integrating our findings within a research framework comprising five consecutive phases. Our analysis suggests that theory-driven feature engineering has utilized a variety of domain theories and feature construction methods. However, current practices lack a structured and consistent approach across the transformation, extraction, and selection stages. Our framework proposes a structured approach to conduct theory-driven feature engineering effectively in machine learning. Our study also identifies existing research gaps in this domain.
Recommended Citation
Rao, Hui; Liu, Xiang; and Sengupta, Avijit, "Theory-Driven Feature Engineering For Machine Learning Method: A Scoping Review and Future Research" (2026). ECIS 2026 Proceedings. 10.
https://aisel.aisnet.org/ecis2026/litrev/litrev/10
Theory-Driven Feature Engineering For Machine Learning Method: A Scoping Review and Future Research
The traditional data-driven feature engineering approach often overlooks essential factors that are not explicitly present in the data and can only be identified with the help of domain knowledge. Such approaches often lead to irrelevant or poorly defined features, which in turn hinder model performance and interpretability. Existing research often employs a “theory-driven feature engineering” approach to incorporate domain knowledge, leveraging domain theories to design features for machine learning models. We reviewed the literature on theory-driven feature engineering while integrating our findings within a research framework comprising five consecutive phases. Our analysis suggests that theory-driven feature engineering has utilized a variety of domain theories and feature construction methods. However, current practices lack a structured and consistent approach across the transformation, extraction, and selection stages. Our framework proposes a structured approach to conduct theory-driven feature engineering effectively in machine learning. Our study also identifies existing research gaps in this domain.
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