5 New Feature Selection Books Reshaping the Industry in 2025

Jing Li, a specialist in machine learning and IoT security, explores the delicate balance between feature selection and feature extraction within IoT intrusion detection systems. The book dives into how these techniques impact classification accuracy, runtime efficiency, and model robustness using the Network TON-IoT dataset. You’ll gain insight into optimizing lightweight machine learning models tailored to resource-constrained IoT devices, understanding when feature extraction outperforms selection and vice versa. This detailed comparison benefits cybersecurity professionals and data scientists focused on enhancing IoT defenses through informed feature reduction strategies.

When Dennis Jingle recognized the increasing complexity in data preprocessing, he crafted this guide to tackle feature selection head-on. You gain a clear understanding of key techniques like filter, wrapper, and embedded methods, alongside practical code examples in Python and R that help you apply these concepts immediately. The book doesn't just explain algorithms; it also addresses challenges like high-dimensional data and ethical concerns such as bias in feature choice. If you work with machine learning models or want to improve your data pipeline's efficiency and fairness, this book offers precise insights without unnecessary jargon.

This tailored book explores the latest advances in feature selection techniques emerging in 2025, focusing on your unique background and goals. It examines new algorithms, evaluation metrics, and practical applications that match your specific interests, helping you engage deeply with the most relevant innovations. By concentrating on cutting-edge discoveries, the book reveals how these developments improve model accuracy and computational efficiency in real-world scenarios, from IoT security to bioinformatics. This personalized approach ensures you gain targeted insights aligned with your expertise and aspirations, making complex topics accessible and immediately useful for your projects.

What happens when an expert in video processing turns to the challenge of face recognition? Shirley C P combines her deep knowledge of computer vision with a focus on video key frames to unpack the complex interplay of feature selection and facial detection. You’ll explore detailed techniques for video summarization, motion analysis, and multimedia content retrieval, gaining insight into how video frames can be selected and analyzed for efficient recognition. This book suits you if you work with video analytics or AI-driven surveillance and want to understand the practical and theoretical foundations behind these rapidly evolving technologies.

After analyzing recent developments in deep neural networks, Frederic Ros and Rabia Riad developed this book to guide you through the nuanced world of feature selection and dimensionality reduction specifically for clustering tasks. You'll gain insight into how deep learning architectures can be leveraged to uncover meaningful patterns in unlabeled data, with chapters synthesizing influential techniques and practical "tricks" from the latest research. The book breaks down complex methods into approachable segments, making it especially suitable if you're an AI engineer or researcher aiming to enhance knowledge discovery in unsupervised or semi-supervised settings. Expect a systematic exploration of families of deep clustering methods, backed by multi-criteria analysis that sharpens your understanding of when and how to apply these approaches.

Anil T's exploration into protein sequence data within bacterial knowledge bases tackles a niche yet vital challenge in computational biology. You learn how to identify meaningful subsets of protein sequence features—crucial for efficient modeling—while avoiding the pitfalls of redundant or irrelevant data that can bog down analysis. The book zeroes in on methods to streamline complex protein datasets, which is particularly useful if you're working on bacterial function analysis or bioinformatics modeling. While concise, it offers focused insight that benefits data scientists and researchers dealing with high-dimensional biological data sets.

This tailored book explores the evolving landscape of feature selection, focusing on emerging trends set to shape 2025 and beyond. It examines innovative techniques through a lens that matches your background and specific interests, ensuring the content is directly relevant to your goals. The book delves into new discoveries and research breakthroughs, revealing how they impact practical applications and machine learning workflows. By concentrating on your unique learning objectives, this personalized guide navigates complex concepts and cutting-edge developments with clarity and enthusiasm. It offers an engaging pathway to mastering future-ready feature selection methods, empowering you to stay current in this fast-paced field with a focus that truly matters to you.