Feature Engineering in Modern Predictive Modeling: Techniques, Automation Frameworks and Emerging Trends

Year : 2026 | Volume : 17 | Issue : 02 | Page : 32 40
By

Jyoti Panchariya,

Surendra Kumar Yadav,

Shruti Mathur,

  1. Scholar, Department of Computer Science and Engineering, JECRC University, Rajasthan, India
  2. Professor, Department of Computer Science and Engineering, JECRC University, Rajasthan, India
  3. Assistant Professor, Department of Computer Science and Engineering, JECRC University, Rajasthan, India

Abstract

Feature engineering plays a crucial role in developing reliable and high-performing predictive models. It involves transforming raw and unprocessed data into meaningful features that help machine learning algorithms achieve better accuracy and overall performance. Feature engineering is helpful in identifying hidden patterns, removing noise, and increasing the signal-to-noise ratio in data sets. Machine learning techniques have progressed remarkably over the years, and the effectiveness of these models largely depends on how informative and well-structured the extracted features are. This paper presents a comprehensive review of the traditional and advanced techniques in feature engineering for building predictive models. Techniques in Feature Engineering: Feature selection techniques, Feature extraction techniques, Feature construction techniques, Feature transformation techniques, Feature scaling techniques. Furthermore, new developments in feature engineering techniques such as Automated Feature Engineering (AutoFE), Deep Feature Synthesis, feature generation techniques in AutoML, etc., are reviewed. Moreover, recent developments in the domain of explainable artificial intelligence, such as feature importance techniques like SHAP values and permutation importance, are reviewed, considering the importance of the role played by explainability in machine learning systems to tackle key challenges such as high-dimensional data, feature redundancy, privacy, etc., in machine learning systems. Finally, new directions for future work are suggested, such as federated feature engineering, causal feature discovery, graph-based learning, etc. This review article aims to assist researchers and practitioners in building a systematic understanding of various feature engineering techniques.

Keywords: Graph neural networks, AutoML, feature tools, SHAP, federated learning, feature engineering

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article: Jyoti Panchariya, Surendra Kumar Yadav, Shruti Mathur. Feature Engineering in Modern Predictive Modeling: Techniques, Automation Frameworks and Emerging Trends. Journal of Computer Technology & Applications. 2026; 17(02):32-40.
How to cite this URL: Jyoti Panchariya, Surendra Kumar Yadav, Shruti Mathur. Feature Engineering in Modern Predictive Modeling: Techniques, Automation Frameworks and Emerging Trends. Journal of Computer Technology & Applications. 2026; 17(02):32-40. Available from: https://journals.stmjournals.com/jocta/article=2026/view=260238

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Regular Issue Subscription Review Article
Volume 17
Issue 02
Received 05/02/2026
Accepted 09/06/2026
Published 10/08/2026
Publication Time 186 Days

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