Improving Classifier Generalization

The book "Improving Classifier Generalization" provides a comprehensive analysis of techniques to enhance generalization performance in classification approaches. It covers a variety of methods for optimizing classification accuracy through numerous case studies, ranging from datasets from the UCI repository to challenges in predictive maintenance and cancer diagnosis. A particular focus is placed on detailed guidance for handling time series data and the discussion of two real-world case studies in the field of condition monitoring. Additionally, various aspects that data scientists must consider before establishing their approach to a classification problem are highlighted. The book also offers an overview of the current state of the art in improving classification generalization and presents the authors' own contributions, including innovative classifiers and approaches to integrating deep learning into fuzzy rule-based classifiers. This work is a valuable reference for researchers and students in the fields of machine learning, health monitoring, predictive maintenance, time series analysis, and classification of gene expression data.