Nature-Inspired Computation in Data Mining and Machine Learning

Beschreibung

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
Provides a timely review and summary of the latest developments in nature-inspired computation and its application in data mining and machine learning Discusses key directions in topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, support vector machine, supervised learning, neural networks, logistic regression, feature selection and extraction, image processing and pattern recognition Reviews both theoretical studies and applications, highlighting how nature-inspired computation combines with traditional techniques in data mining and machine learning to produce enhanced performance Includes case studies from various applications and industries

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Xin-She Yang

Themen in »Nature-Inspired Computation in Data Mining and Machine Learning«

Nature-inspired Algorithm Bio-inspired Computation Bioinformatics Data Mining Evolutionary Computing Machine Learning Data Modeling Heuristics Pattern Recognition

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