AI-Driven Automated Feature Engineering to Enhance Performance of Predictive Models in Data Science - Sarath Babu Dodda, Srihari Maruthi, Mohan Raparthi, Surendranadha Reddy Byrapu Reddy (International Journal of Control and Automation, 2020)
In the rapidly evolving landscape of data science, predictive modeling stands as a cornerstone for deriving actionable insights from vast amounts of data. Central to the success of predictive modeling is the process of feature engineering, which involves selecting, transforming, and creating features to improve model performance. With the advent of artificial intelligence (AI) and machine learning (ML), automated feature engineering has emerged as a promising approach to streamline and enhance this critical process. This paper explores the role of AI-driven automated feature engineering techniques in augmenting the performance of predictive models in data science.The paper begins with an overview of predictive modeling in data science, highlighting the significance of feature engineering in model development. [1] Traditional approaches to feature engineering often rely on manual experimentation and domain expertise, which can be time-consuming and prone to human bias. In contrast, AI-driven automated feature engineering leverages ML algorithms and techniques to automate and optimize the feature engineering process, reducing the need for manual intervention and accelerating model development. Various AI-driven automated feature engineering techniques are examined, including machine learning-based feature selection algorithms, automated feature transformation methods, generative adversarial networks (GANs) for feature creation, and deep learning-based feature extraction techniques. These methods offer advantages such as improved model performance, time and resource efficiency, and reduced human bias. The paper also discusses challenges and limitations associated with AI-driven automated feature engineering, such as data quality requirements, interpretability of automated features, and the risk of overfitting. Additionally, case studies and applications demonstrate the practical utility of automated feature engineering across diverse domains, including finance, healthcare, marketing, and more.The paper explores future directions in automated feature engineering, including emerging trends, integration of domain knowledge, and ethical considerations. By providing valuable insights into the methodologies, tools, benefits, challenges, and future prospects of AI-driven automated feature engineering, this paper aims to guide practitioners and researchers in harnessing advanced techniques to enhance predictive modeling in data science.