Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Theses
      • Higher Degree Theses
      • View Item
      •   Research Commons
      • University of Waikato Theses
      • Higher Degree Theses
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Meta-Learning and the Full Model Selection Problem

      Sun, Quan
      Thumbnail
      Files
      thesis.pdf
      3.487Mb
      Citation
      Export citation
      Sun, Q. (2014). Meta-Learning and the Full Model Selection Problem (Thesis, Doctor of Philosophy (PhD)). University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/8520
      Permanent Research Commons link: https://hdl.handle.net/10289/8520
      Abstract
      When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several projects needed to be done in a relatively short time.

      Naturally, as a computer science graduate, I started to ask myself, "What can be automated here?"; because, intuitively, part of my work is more or less a loop that can be programmed. Literally, the loop is "choose, run, test and choose again... until some criterion/goals are met".

      In other words, I use my experience or knowledge about machine learning and data mining to guide and speed up the process of selecting and applying techniques in order to build a relatively good predictive model for a given dataset for some purpose. So the following questions arise:

      "Is it possible to design and implement a system that helps a data analyst to choose from a set of data mining tools? Or at least that provides a useful recommendation about tools that potentially save some time for a human analyst."

      To answer these questions, I decided to undertake a long-term study on this topic, to think, define, research, and simulate this problem before coding my dream system. This thesis presents research results, including new methods, algorithms, and theoretical and empirical analysis from two directions, both of which try to propose systematic and efficient solutions to the questions above, using different resource requirements, namely, the meta-learning-based algorithm/parameter ranking approach and the meta-heuristic search-based full-model selection approach.

      Some of the results have been published in research papers; thus, this thesis also serves as a coherent collection of results in a single volume.
      Date
      2014
      Type
      Thesis
      Degree Name
      Doctor of Philosophy (PhD)
      Supervisors
      Pfahringer, Bernhard
      Mayo, Michael
      Publisher
      University of Waikato
      Rights
      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.
      Collections
      • Higher Degree Theses [1714]
      Show full item record  

      Usage

      Downloads, last 12 months
      80
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement