Title page for etd-0614117-091652


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URN etd-0614117-091652
Author Jia-Liang Guo
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 2
Degree Master
Type of Document
Language English
Title Process Discovery using Rule-Integrated Trees Hidden Semi-Markov Models
Date of Defense 2017-07-13
Page Count 53
Keyword
  • Process discovery
  • Classification Tree Hidden Semi-Markov Model
  • HSMM
  • Rule-Integrated trees
  • Random forest
  • Abstract To predict or to explain? With the dramatical growth of the volume of
    information generated from various information systems, data science has become
    popular and important in recent years while machine learning algorithms provide a very
    strong support and foundation for various data applications. Many data applications are
    based on black-box models. For example, a fraud detection system can predict which
    person will default but we cannot understand how the system consider it’s fraud. While
    white-box models are easy to understand but have relatively poor predictive
    performance. Hence, in this thesis, we propose a novel grafted tree algorithm to
    integrate trees of random forests. The model attempt to find a balance between a
    decision tree and a random forest. That is, the grafted tree have better interpretability
    and the performance than a single decision tree.
    With the decision tree is integrated from a random forest, it will be applied to
    Hidden semi-Markov models (HSMM) to build a Classification Tree Hidden Semi-
    Markov Model (CTHSMM) in order to discover underlying changes of a system. The experimental result shows that our proposed model RITHSMM is better than a simple
    decision tree based on Classification and Regression Trees and it can find more
    states/leaves so as to answer a kind of questions, “given a sequence of observable
    sequence, what are the most probable/relevant sequence of changes of a dynamic
    system?”.
    Advisory Committee
  • Keng-Pei Lin - chair
  • Pei-Ju Lee - co-chair
  • Yihuang Kang - advisor
  • Files
  • etd-0614117-091652.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2017-07-14

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