Comparative Study

  • Oded Maimon
  • Mark Last
Chapter
Part of the Massive Computing book series (MACO, volume 1)

Abstract

This section compares the performance of the information-theoretic network to leading classification methods of data mining. The comparison is based on public datasets and it includes the following performance criteria:
  • Dimensionality Reduction is measured by the portion of candidate-input attributes removed by the algorithm (excluded from the network) and by the size of the information-theoretic network vs. other predictive models.

  • Prediction Accuracy is the average accuracy of the network on validation cases vs. published accuracy of other classifiers.

  • Stability represents the ability of the algorithms to provide similar results from different random samples of the same dataset. The benchmark classification methods used for the comparison include:

  • Naive Bayes Classifier. This is a probabilistic method assuming conditional independence of all input attributes. See the details of the algorithm in (Mitchell, 1997).

  • C4.5. This is a state-of-the-art decision tree algorithm presented in (Quinlan, 1993). Today, most commercial tools for constructing decision trees are based on C4.5 or one of its modified versions.

  • CART ™ This is an earlier decision tree method (Breiman et al., 1984). It is used as the engine of a commercial tool, having the same name, which is available from Salford Systems ( http://www.salford-systems.com/ ).

Keywords

Dimensionality Reduction Terminal Node Training Error Conditional Entropy Input Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Oded Maimon
    • 1
  • Mark Last
    • 2
  1. 1.Tel-Aviv UniversityTel-AvivIsrael
  2. 2.University of South FloridaTampaUSA

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