Data Classification: Algorithms and Applications

Comprehensive Coverage of the Entire Area of Classification

Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data.

This comprehensive book focuses on three primary aspects of data classification:

• Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks.

• Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm.

• Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Table of Contents

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 Classification
Chapter 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 Classification