Computational Intelligence for High-Dimensional Machine Learning : A Feature Selection Perspective and Its Real-World Applications

Yu Zhou

ISBN 10: 9819626862 / ISBN 13: 9789819626861

Editore: Springer, Springer, 2025

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9789819626861

Dati bibliografici

• Titolo: Computational Intelligence for High-Dimensional Machine Learning : A Feature Selection Perspective and Its Real-World Applications
• Editore: Springer, Springer
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 9819626862
• ISBN 13: 9789819626861
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 8 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 8 millimetri di profondità
• Peso: 237 grammi

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Riassunto

This book focuses on the modelling and optimization aspects of the feature selection problem through computational intelligence methods in complex, high-dimensional supervised machine learning.

Informazioni sull?autore

Prof. Yu Zhou received Ph.D. degree in computer science from the City University of Hong Kong, Hong Kong, in 2017. From 2016 to 2017, he was a research associate, postdoctoral research fellow and visiting scholar in City University of Hong Kong. In 2017, he joined College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China, as an assistant professor. He is currently a tenured associate professor. His current research interests include computational intelligence, machine learning and intelligent information processing. He has co-authored over 50 international journal and conference papers, including IEEE TEVC, IEEE TCYB, IEEE TIM, IEEE TETCI, IEEE IOTJ, etc. He was the recipient of outstanding paper award from Computer Academy of Guangdong and the outstanding reviewer award of EJOR.

Prof. Xiao Zhang received the B.Eng. and M.Eng. degrees from South-Central Minzu University, Wuhan, China, in 2009 and 2011, respectively. In 2015, he was a visiting scholar with the Utah State University, Utah, USA. He received his Ph.D. degree from Department of Computer Science in City University of Hong Kong, Hong Kong, 2016. During 2016–2019, he was a postdoc research fellow at Singapore University of Technology and Design. Currently, he is an associate professor with College of Computer Science, South-Central Minzu University, China. His research interests include algorithms design and analysis, combinatorial optimization, wireless and UAV networking.

Prof. Sam Kwong is the chair professor of Computational Intelligence and concurrently as associate vice-president (Strategic Research) of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as one of the top 2% of the world’s most cited scientists, according to the Stanford University report. He was listed as one of the top 1% of the world’s most cited scientists by Clarivate in 2022. He has also been actively engaged in knowledge transfer  between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding.