Computational Intelligence for High-Dimensional Machine Learning

This book focuses on themodelling and optimization aspects of the feature selection problem throughcomputational intelligence methods in complex, high-dimensional supervisedmachine learning. To aidreaders in conductingresearch in this field, it covers fundamental concepts and state-of-the-artalgorithms. This book also providesa detailed insight into applying these algorithms into real-world applications.The authors begin by introducing thedefinition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve intodimension reduction methods for high-dimensional ML, including global and localfeature selection (FS) techniques. This book also comprehensively presents computationalintelligence methods such as evolutionary computation and deep neural networksfor FS, supported by boththeoretical and empirical evidence. Furthermore, this book explores real-worldscenario applications involving high-dimensional ML, particularly in the context of smartcities, bioinformatics and industrial informatics. This book is a suitable readfor postgraduates and researchers who are interested in the research areas ofcomputational intelligence, soft computing, machine learning and deep learning.Professionals and practitioners within these related fields will also benefitfrom this book.