Title page for etd-0727118-134901


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URN etd-0727118-134901
Author Bo-Wen Kuo
Author's Email Address No Public.
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Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title Interpretable representation learning based on Deep Rule Forests
Date of Defense 2018-07-20
Page Count 59
Keyword
  • Rule Learning
  • Random Forest
  • Representation Learning
  • Interpretability
  • Deep Rule Forest
  • Abstract The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in higher predictive models, but may sacrifice for model interpretability. On the other hand, the spirit of representation learning is to extract abstractive concepts from manifestations of the data.
    For instance, Deep Neural networks (DNNs) is the most popular method in representation learning. However, unaccountable feature representation is the shortcoming of DNNs. In this paper, we proposed an approach, Deep Rule Forest (DRF), to learn region representations based on random forest in the deep layer-wise structures. The learned interpretable rules region representations combine other machine learning algorithms. We trained CART which learned from DRF region representations, and found that the prediction accuracies sometime are better than ensemble learning methods.
    Advisory Committee
  • Keng-Pei Lin - chair
  • Pei-Ju, Lee - co-chair
  • Yihuang, Kang - advisor
  • Files
  • etd-0727118-134901.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2018-08-28

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