Advances in Machine Learning for Big Data Analysis

This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. It presents various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance.

Format: Hardback
Length: 239 pages
Publication date: 25 February 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the cutting-edge research aspects of ensemble approaches to machine learning techniques, offering invaluable insights for addressing the complex challenges posed by big data. Spanning multiple chapters, it presents a diverse range of machine learning algorithms designed to extract data-driven decisions from vast datasets across diverse domains, including banking, healthcare, social media, and video surveillance. Each chapter focuses on a specific functionality, enabling the book to serve as a valuable resource for advancing the field of machine learning and data science in solving a wide array of big data problems.

While there has been significant progress in the advancement of machine learning in various domains such as biomedical, stock prediction, and sentiment analysis, there has been limited exploration of the application of advanced machine learning techniques to tackle big data challenges. This book aims to bridge that gap by providing a comprehensive exploration of ensemble approaches and their potential to revolutionize the way we approach big data analysis.

The book begins by introducing the concept of ensemble approaches and their role in improving the accuracy and reliability of machine learning models. It then delves into the various ensemble techniques, including bagging, boosting, and voting, and their applications in different domains. Each chapter provides detailed explanations of the algorithms, their principles, and their performance metrics, enabling readers to gain a deep understanding of the underlying mechanisms and practical applications of ensemble approaches.

Furthermore, the book discusses the challenges associated with big data analysis, such as data heterogeneity, noise, and complexity. It presents strategies and techniques for addressing these challenges, including feature selection, dimensionality reduction, and model fusion. By incorporating these techniques, readers can develop robust and efficient machine learning models that can handle large datasets and deliver accurate predictions.

In conclusion, this book is a must-read for researchers, practitioners, and students interested in the field of machine learning and data science. It offers a comprehensive and up-to-date exploration of ensemble approaches to machine learning techniques, providing valuable insights and practical applications for addressing the complex challenges posed by big data. With its extensive coverage, detailed explanations, and practical examples, it serves as a valuable resource for advancing the field and driving innovation in data-driven decision-making.

Weight: 559g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811689291
Edition number: 1st ed. 2022