AI-Enhanced Feature Selection and Dimensionality Reduction in Project Management: Optimizing Model Performance for Adaptive Change Strategies

Abstract

Feature selection and dimensionality reduction are critical steps in machine learning model development, particularly within AI-driven frameworks for Change Management and Project Management. These techniques not only improve model performance but also enhance computational efficiency, making them invaluable in managing large datasets involved in project workflows and change adaptation strategies. This paper provides a comprehensive overview of various techniques in these domains, analyzing their impact on both machine learning models and their applications in optimizing project management outcomes. We explore methods such as filter, wrapper, and embedded approaches for feature selection, along with principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders for dimensionality reduction. Through empirical evaluations and project-focused case studies, we highlight the strengths and limitations of each technique in driving efficient decision-making and supporting adaptive project management in dynamic environments.