Abstract
Neural Network Models for Feature Extraction and Empirical Thresholding
study the combination of neural network models and empirical thresholding methods to
improve the procedure for extracting features. For researchers and practitioners
working in the fields of feature extraction and machine learning, it illustrates the
advantages, approaches, and difficulties connected with this integration and offers
helpful insights. The basic concepts of feature extraction are covered in this book
chapter, along with an overview of the several neural network models that can be used
to accomplish this task, such as auto-encoders, convolutional neural networks (CNNs),
and recurrent neural networks (RNNs). This book chapter emphasizes the benefits,
methodologies, and challenges associated with this integration, providing valuable
insights for researchers and practitioners in the fields of feature extraction and machine
learning. This book chapter is useful for statistical analysis, domain expertise-driven
threshold selection, and validation metrics-based threshold choice as efficient
techniques for enhancing feature quality and lowering noise.