Computational Methods of Feature Selection
eBook - PDF

Computational Methods of Feature Selection

  1. 440 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Computational Methods of Feature Selection

Book details
Table of contents
Citations

About This Book

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the

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Yes, you can access Computational Methods of Feature Selection by Huan Liu, Hiroshi Motoda, Huan Liu, Hiroshi Motoda in PDF and/or ePUB format, as well as other popular books in Economics & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2007
ISBN
9781584888796
Edition
1
Topic
Economics
Subtopic
Statistics for Business & Economics
Index
Economics

Table of contents

  1. Cover
  2. Title
  3. Copyright
  4. Preface
  5. Acknowledgments
  6. Contributors
  7. Contents
  8. Part I: Introduction and Background
  9. Chapter 1: Less Is More
  10. Chapter 2: Unsupervised Feature Selection
  11. Chapter 3: Randomized Feature Selection
  12. Chapter 4: Causal Feature Selection
  13. Part II: Extending Feature Selection
  14. Chapter 5: Active Learning of Feature Relevance
  15. Chapter 6: A Study of Feature Extraction Techniques Based on Decision Border Estimate
  16. Chapter 7: Ensemble-Based Variable Selection Using Independent Probes
  17. Chapter 8: Efficient Incremental-Ranked Feature Selection in Massive Data
  18. Part III: Weighting and Local Methods
  19. Chapter 9: Non-Myopic Feature Quality Evaluation with (R)ReliefF
  20. Chapter 10: Weighting Method for Feature Selection in K-Means
  21. Chapter 11: Local Feature Selection for Classification
  22. Chapter 12: Feature Weighting through Local Learning
  23. Part IV: Text Classification and Clustering
  24. Chapter 13: Feature Selection for Text Classification
  25. Chapter 14: A Bayesian Feature Selection Score Based on NaĂŻve Bayes Models
  26. Chapter 15: Pairwise Constraints-Guided Dimensionality Reduction
  27. Chapter 16: Aggressive Feature Selection by Feature Ranking
  28. Part V: Feature Selection in Bioinformatics
  29. Chapter 17: Feature Selection for Genomic Data Analysis
  30. Chapter 18: A Feature Generation Algorithm with Applications to Biological Sequence Classification
  31. Chapter 19: An Ensemble Method for Identifying Robust Features for Biomarker Discovery
  32. Chapter 20: Model Building and Feature Selection with Genomic Data
  33. Index