Decision Forests with Oblique Decision Trees

  • Peter J. Tan
  • David L. Dowe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


Ensemble learning schemes have shown impressive increases in prediction accuracy over single model schemes. We introduce a new decision forest learning scheme, whose base learners are Minimum Message Length (MML) oblique decision trees. Unlike other tree inference algorithms, MML oblique decision tree learning does not over-grow the inferred trees. The resultant trees thus tend to be shallow and do not require pruning. MML decision trees are known to be resistant to over-fitting and excellent at probabilistic predictions. A novel weighted averaging scheme is also proposed which takes advantage of high probabilistic prediction accuracy produced by MML oblique decision trees. The experimental results show that the new weighted averaging offers solid improvement over other averaging schemes, such as majority vote. Our MML decision forests scheme also returns favourable results compared to other ensemble learning algorithms on data sets with binary classes.


Random Forest Leaf Node Base Learner Minimum Description Length Ensemble Learning 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter J. Tan
    • 1
  • David L. Dowe
    • 1
  1. 1.School of Computer Science and Software EngineeringMonash UniversityClaytonAustralia

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