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A greedy algorithm for supervised discretization.

Richard Butterworth1, Dan A Simovici, Gustavo S Santos

  • 1Department of Computer Science, University of Massachusetts at Boston, Boston, MA 02125, USA. rickb@cs.umb.edu <rickb@cs.umb.edu>

Journal of Biomedical Informatics
|October 7, 2004
PubMed
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We developed a greedy algorithm for supervised discretization, transforming data for machine learning classifiers. This method effectively prepares data with nominal attributes, enhancing classifier performance.

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Supervised discretization is crucial for preparing continuous data into discrete intervals for machine learning algorithms.
  • Classifiers like decision trees and naive Bayes often perform better with nominal attributes.

Purpose of the Study:

  • To introduce a novel greedy algorithm for supervised discretization.
  • To provide a method for transforming continuous data into a format suitable for nominal attribute-based classifiers.

Main Methods:

  • A greedy algorithm approach was employed for supervised discretization.
  • A metric was defined on the space of partitions to guide the discretization process.
  • The algorithm was tested using decision trees and naive Bayes classifiers.

Related Experiment Videos

Main Results:

  • The proposed supervised discretization algorithm demonstrated effectiveness.
  • Experimental results confirmed the algorithm's utility in preparing data for classifiers.
  • The method successfully generated nominal attributes from continuous data.

Conclusions:

  • The greedy algorithm offers an efficient and effective solution for supervised discretization.
  • This technique enhances the performance of classifiers requiring nominal attributes.
  • The study validates the algorithm's practical applicability in machine learning workflows.