Rough Sets and Knowledge Technology Lecture Notes in Computer Science 4481 Lecture Notes in Artificial Intelligence 2nd edition by Yiyu Yao, JingTao Yao, Pawan Lingras, Wei-Zhi Wu, Marcin Szczuka, Nick Cercone, Dominik Ślȩzak ISBN 3540724575   978-3540724575

Rough Sets and Knowledge Technology (Lecture Notes in Computer Science 4481 Lecture Notes in Artificial Intelligence) 2nd edition by Yiyu Yao, JingTao Yao, Pawan Lingras, Wei-Zhi Wu, Marcin Szczuka, Nick Cercone, Dominik Ślȩzak  – Ebook PDF Instant Download/Delivery. ‎ 3540724575   978-3540724575
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ISBN 10: ‎ 3540724575
ISBN 13:  978-3540724575
Author: Yao, JingTao Yao, Pawan Lingras, Wei-Zhi Wu, Marcin Szczuka, Nick Cercone, Dominik Ślȩzak 

This book constitutes the refereed proceedings of the Second International Conference on Rough Sets and Knowledge Technology, RSKT 2007, held in Toronto, Canada in May 2007 in conjunction with the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2007, both as part of the Joint Rough Set Symposium, JRS 2007.

Rough Sets and Knowledge Technology (Lecture Notes in Computer Science 4481 Lecture Notes in Artificial Intelligence) 2nd Table of contents:

Preface

• Overview of Rough Set Theory
• Relevance of Rough Sets in Knowledge Technology
• Acknowledgments and Introduction to Contributions

Chapter 1: Foundations of Rough Set Theory

• Basic Concepts in Rough Sets
• Approximation Spaces and Indiscernibility Relations
• Relation to Classical Set Theory
• Historical Development of Rough Set Theory

Chapter 2: Knowledge Representation and Rough Set Theory

• Knowledge Representation Models in Rough Sets
• Information Systems and Knowledge Discovery
• Rough Sets and Their Use in Representing Uncertainty
• Approximation Theory and Information Granulation

Chapter 3: Rough Set-Based Algorithms

• Algorithms for Knowledge Discovery Using Rough Sets
• Feature Selection and Dimensionality Reduction
• Rule Induction from Rough Sets
• Decision Tables and Inductive Learning

Chapter 4: Rough Set Theory in Data Mining

• Application of Rough Sets to Data Mining Tasks
• Classifiers and Predictive Models Based on Rough Sets
• Data Preprocessing and Noise Handling in Rough Sets
• Case Studies in Real-World Data Mining Problems

Chapter 5: Extensions of Rough Set Theory

• Fuzzy Rough Sets
• Probabilistic Rough Sets
• Temporal Rough Sets
• Hybrid Models (Neuro-Fuzzy Systems with Rough Sets)

Chapter 6: Rough Sets and Decision-Making

• Rough Sets in Decision Support Systems
• Decision Trees and Rule-Based Decision Making
• Risk and Uncertainty Modeling in Decision Systems
• Applications of Rough Set Theory in Decision Analysis

Chapter 7: Applications of Rough Sets

• Applications in Bioinformatics and Healthcare
• Rough Sets in Financial Modeling and Risk Assessment
• Marketing and Customer Relationship Management
• Industrial Process Control and Automation

Chapter 8: Rough Sets and Machine Learning

• Integrating Rough Sets with Machine Learning Algorithms
• Rough Sets in Neural Networks and Genetic Algorithms
• Evaluation and Performance Metrics for Rough Set Models
• Case Studies in Pattern Recognition and AI Applications

Chapter 9: Challenges and Future Directions

• Open Problems and Research Challenges in Rough Set Theory
• Future Trends in Knowledge Discovery with Rough Sets
• Interdisciplinary Applications and Collaborations
• The Role of Rough Sets in AI and Machine Learning

Conclusion

• Summary of Contributions
• Implications for Knowledge Technology
• Final Remarks on the Impact of Rough Sets in AI

Appendices

• Glossary of Key Terms
• List of Contributors
• Further Reading and References
• Index

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