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T3: A Classification Algorithm for Data Mining

  • Christos Tjortjis
  • John Keane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

This paper describes and evaluates T3, an algorithm that builds trees of depth at most three, and results in high accuracy whilst keeping the size of the tree reasonably small. T3 is an improvement over T2 in that it builds larger trees and adopts a less greedy approach. T3 gave better results than both T2 and C4.5 when run against publicly available data sets: T3 decreased classification error on average by 47% and generalisation error by 29%, compared to T2; and T3 resulted in 46% smaller trees and 32% less classification error compared to C4.5. Due to its way of handling unknown values, T3 outperforms C4.5 in generalisation by 99% to 66%, on a specific medical dataset.

Keywords

Classification Accuracy Classification Algorithm Tree Size Classification Error Continuous 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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Christos Tjortjis
    • 1
  • John Keane
    • 1
  1. 1.UMISTDepartment of ComputationManchesterUK

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