Chapter 10. Classification

  1. Carlo Vercellis

Published Online: 17 MAR 2009

DOI: 10.1002/9780470753866.ch10

Business Intelligence: Data Mining and Optimization for Decision Making

Business Intelligence: Data Mining and Optimization for Decision Making

How to Cite

Vercellis, C. (2009) Classification, in Business Intelligence: Data Mining and Optimization for Decision Making, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470753866.ch10

Author Information

  1. Politecnico di Milano, Italy

  1. This Chapter was Co-Written by Carlotta Orsenigo

Publication History

  1. Published Online: 17 MAR 2009
  2. Published Print: 20 MAR 2009

ISBN Information

Print ISBN: 9780470511381

Online ISBN: 9780470753866

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Keywords:

  • classification models - supervised learning methods for predicting value of categorical target attribute;
  • classification problem;
  • taxonomy of classification models;
  • discriminant analysis, perceptron methods, neural networks and support vector machines;
  • classification model evaluation;
  • repeated random sampling;
  • confusion matrices;
  • Receiver operating characteristic (ROC) curve charts;
  • classification trees - best-known learning methods in data mining applications;
  • Bayesian methods and probabilistic classification models

Summary

This chapter contains sections titled:

  • Classification problems

  • Evaluation of classification models

  • Classification trees

  • Bayesian methods

  • Logistic regression

  • Neural networks

  • Support vector machines

  • Notes and readings