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Data classification : algorithms and applications / edited by Charu C. Aggarwal, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA.
- Format:
- Book
- Series:
- Chapman & Hall/CRC data mining and knowledge discovery series ; Volume 35.
- Chapman & Hall/CRC data mining and knowledge discovery series ; Volume 35
- Language:
- English
- Subjects (All):
- File organization (Computer science).
- Categories (Mathematics).
- Algorithms.
- Physical Description:
- 1 online resource (704 p.)
- Edition:
- 1st edition
- Other Title:
- Algorithms and applications
- Place of Publication:
- Boca Raton : CRC Press, [2015]
- Language Note:
- English
- System Details:
- text file
- Summary:
- This book homes in on three primary aspects of data classification: the core methods for data classification including probabilistic classification, decision trees, rule-based methods, and SVM methods; different problem domains and scenarios such as multimedia data, text data, biological data, categorical data, network data, data streams and uncertain data: and different variations of the classification problem such as ensemble methods, visual methods, transfer learning, semi-supervised methods and active learning. These advanced methods can be used to enhance the quality of the underlying classification results-- Provided by publisher.
- Contents:
- Front Cover; Dedication; Contents; Editor Biography; Contributors; Preface; Chapter 1: An Introduction to Data Classification; Chapter 2: Feature Selection for Classification: A Review; Chapter 3: Probabilistic Models for Classification; Chapter 4: Decision Trees: Theory and Algorithms; Chapter 5: Rule-Based Classification; Chapter 6: Instance-Based Learning: A Survey; Chapter 7: Support Vector Machines; Chapter 8: Neural Networks: A Review; Chapter 9: A Survey of Stream Classification Algorithms; Chapter 10: Big Data Classification; Chapter 11: Text Classification
- Chapter 12: Multimedia ClassificationChapter 13: Time Series Data Classification; Chapter 14: Discrete Sequence Classification; Chapter 15: Collective Classification of Network Data; Chapter 16: Uncertain Data Classification; Chapter 17: Rare Class Learning; Chapter 18: Distance Metric Learning for Data Classification; Chapter 19: Ensemble Learning; Chapter 20: Semi-Supervised Learning; Chapter 21: Transfer Learning; Chapter 22: Active Learning: A Survey; Chapter 23: Visual Classification; Chapter 24: Evaluation of Classification Methods
- Chapter 25: Educational and Software Resources for Data ClassificationColor Insert
- Notes:
- Description based upon print version of record.
- Includes bibliographical references.
- Description based on print version record.
- ISBN:
- 9781498760584
- 1498760589
- 9780429102639
- 0429102631
- 9781466586741
- 1466586745
- OCLC:
- 890721171
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