Optimized Machine Learning Pipelines: Leveraging RFE, ELM, and SRC for Advanced Software Development in AI Applications

Abstract

Background Machine learning has become critical in AI software development, speeding up data processing and improving predictive insights. Optimized ML pipelines increase accuracy and efficiency, which benefits industries such as healthcare, finance, and automation.
Methods This work uses Recursive Feature Elimination (RFE), Extreme Learning Machine (ELM), and Sparse Representation Classification (SRC) to develop a high-performance ML pipeline for feature selection, quick training, and efficient data representation.
Objectives To evaluate the usefulness of RFE, ELM, and SRC in optimizing AI pipelines by improving feature selection, training speed, and classification accuracy, resulting in an efficient framework for real-time applications.
Results The proposed RFE + ELM + SRC technique outperformed existing models with 95% accuracy and 92% F1 score. This hybrid technique improves machine learning performance for complicated, real-time AI applications.
Conclusion Integrating RFE, ELM, and SRC improves machine learning operations by balancing accuracy and computing efficiency. This optimized pipeline offers a scalable solution for high-performance AI jobs, fulfilling the needs of a variety of fields that require rapid and precise prediction skills.