4 New Feature Extraction Books Reshaping the Industry in 2025

The breakthrough moment came when Cuantum Technologies recognized that traditional feature extraction methods were falling short in modern machine learning contexts. Their book takes you through advanced techniques like polynomial features, interaction terms, and dimensionality reduction, showing how to refine raw data into inputs that significantly boost model accuracy. You’ll gain hands-on skills in automating data preparation with Scikit-Learn pipelines and discover how AutoML tools can streamline feature selection and optimization. This resource suits data scientists and ML engineers ready to deepen their expertise and tackle complex, real-world data challenges with practical, domain-specific case studies.

Drawing from extensive expertise in medical imaging and signal processing, the authors present a fresh approach to feature extraction through novel wavelet filter bank designs. You gain insight into multiplier-free orthogonal wavelet filter-banks, symmetric Daub-4 modifications, and adaptive Gabor wavelet techniques tailored for medical image retrieval. The book details performance evaluations on recognized databases like NEMA and OASIS, offering concrete benchmarks for retrieval precision and computational efficiency. This work suits you if you’re involved in medical image analysis or developing content-based image retrieval systems, aiming to enhance accuracy and speed through innovative feature descriptors.

This tailored book explores the breakthroughs in feature extraction shaping the landscape of 2025. It examines emerging techniques and automation advancements that redefine how raw data transforms into powerful inputs for AI and machine learning models. By focusing on your interests and background, it reveals the newest discoveries relevant to your goals, offering an engaging journey through cutting-edge developments. With a personalized approach, this book covers innovations across diverse domains such as signal processing, image analysis, and IoT security. It invites you to delve into the latest research and practical insights that keep you ahead in this rapidly evolving field, making complex topics accessible and directly aligned with your learning objectives.

What started as a focused investigation into IoT security challenges became a detailed study on optimizing machine learning models through feature reduction techniques. Jing Li and Hewan Chen bring their strong backgrounds in cybersecurity and machine learning to dissect how feature selection and extraction impact intrusion detection in IoT networks. You'll gain practical insights into balancing detection accuracy with computational efficiency, especially in resource-constrained environments common to IoT devices. The book's comparative analysis, including metrics like f1-score and runtime on the TON-IoT dataset, offers clarity on when to prefer feature extraction over selection, or vice versa, tailored to your system's needs. It's a solid read if you're directly involved in developing or improving IoT intrusion detection systems.

Unlike most feature extraction books that focus broadly on theory, this work by Balaji V R and Joby Titus T zeroes in on speech signal processing as the critical gateway to effective speech recognition. You’ll learn how the authors break down complex operations like spectral analysis, parametric transformation, and statistical modeling into understandable components that form the foundation of any speech recognition system. Their approach clarifies how to isolate relevant speech features while filtering out noise, which is vital for anyone working with audio data or voice technologies. This book suits engineers and researchers who want a concise yet focused introduction to the practical mechanics behind speech feature extraction.