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      Smoothing in Probability Estimation Trees

      Han, Zhimeng
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      Han, Z. (2011). Smoothing in Probability Estimation Trees (Thesis, Master of Science (MSc)). University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/5701
      Permanent Research Commons link: https://hdl.handle.net/10289/5701
      Abstract
      Classification learning is a type of supervised machine learning technique that uses a classification model (e.g. decision tree) to predict unknown class labels for previously unseen instances. In many applications it can be very useful to additionally obtain class probabilities for the different class labels. Decision trees that yield these probabilities are also called probability estimation trees (PETs). Smoothing is a technique used to improve the probability estimates. There are several existing smoothing methods, such as the Laplace correction, M-Estimate smoothing and M-Branch smoothing. Smoothing does not just apply to PETs. In the field of text compression, PPM in particular, smoothing methods play a important role. This thesis migrates smoothing methods from text compression to PETs. The newly migrated methods in PETs are compared with the best of the existing smoothing methods considered in this thesis under different experiment setups. Unpruned, pruned and bagged trees are considered in the experiments. The main finding is that the PPM-based methods yield the best probability estimate when used with bagged trees, but not when used with individual (pruned or unpruned) trees.
      Date
      2011
      Type
      Thesis
      Degree Name
      Master of Science (MSc)
      Supervisors
      Frank, Eibe
      Publisher
      University of Waikato
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      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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      • Masters Degree Theses [2387]
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