Instance Selection and Construction for Data Mining: The Springer International

In the era of big data, data mining plays a pivotal role in extracting valuable insights and knowledge from vast amounts of information. However, the quality of the data used in data mining algorithms significantly impacts the reliability and accuracy of the results obtained. Instance selection and construction techniques are essential steps in data preprocessing, as they enable the selection and construction of high-quality datasets that are representative of the underlying data distribution.

Instance Selection and Construction for Data Mining (The Springer International Series in Engineering and Computer Science Book 608)

by Huan Liu

4.1 out of 5

Language	:	English
File size	:	5683 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Print length	:	441 pages

FREE

This book, "Instance Selection and Construction for Data Mining: The Springer International," offers a comprehensive overview of the state-of-the-art techniques in this field. Written by a team of leading experts, this book provides a thorough understanding of the principles and applications of instance selection and construction methods.

Key Features

• Comprehensive coverage: Covers a wide range of instance selection and construction techniques, including filter methods, wrapper methods, and hybrid methods.
• In-depth analysis: Provides a detailed analysis of the strengths and limitations of each method, enabling readers to make informed decisions about the most appropriate technique for their specific application.
• Practical examples: Includes numerous real-world examples and case studies, demonstrating the practical applications of instance selection and construction in various domains.
• Advanced topics: Explores advanced topics such as active learning, ensemble methods, and semi-supervised learning, providing readers with a cutting-edge understanding of the field.

Target Audience

This book is an invaluable resource for data scientists, machine learning practitioners, researchers, and students working in the field of data mining. It is also an excellent reference for anyone interested in improving the quality and efficiency of their data mining models.

Table of Contents

1. to Instance Selection and Construction

• Overview of data mining and instance selection
• Importance of instance selection in data mining
• Types of instance selection methods

• Random sampling
• Stratified sampling
• Cluster-based sampling
• Distance-based sampling
• Density-based sampling

• Forward selection
• Backward selection
• Bidirectional selection
• Genetic algorithms
• Simulated annealing

• Filter-wrapper methods
• Ensemble methods
• Semi-supervised learning

• Active learning
• Cost-sensitive learning
• Imbalanced data learning
• Outlier detection
• Novelty detection

• Real-world examples of instance selection and construction in various domains
• Analysis of the performance and benefits of different methods
• Discussion of the challenges and future directions in instance selection and construction

"Instance Selection and Construction for Data Mining: The Springer International" is an essential guide for anyone looking to improve the quality and efficiency of their data mining models. With its comprehensive coverage of the latest techniques and practical examples, this book provides a solid foundation for data scientists and machine learning practitioners.

Whether you are a seasoned data miner or just starting your journey in this field, this book will empower you with the knowledge and tools you need to unlock the full potential of data mining.

Free Download Your Copy Today

Don't miss out on this invaluable resource for advancing your data mining capabilities. Free Download your copy of "Instance Selection and Construction for Data Mining: The Springer International" today and start transforming your data into actionable insights.

Instance Selection and Construction for Data Mining (The Springer International Series in Engineering and Computer Science Book 608)

by Huan Liu

4.1 out of 5

Language	:	English
File size	:	5683 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Print length	:	441 pages

FREE