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dc.contributor.advisorPfahringer, Bernhard
dc.contributor.advisorMayo, Michael
dc.contributor.authorSun, Quan
dc.date.accessioned2014-02-16T23:10:22Z
dc.date.available2014-02-16T23:10:22Z
dc.date.issued2014
dc.identifier.citationSun, 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/10289/8520en
dc.identifier.urihttps://hdl.handle.net/10289/8520
dc.description.abstractWhen 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectmeta-learning
dc.subjectranking
dc.subjectensemble learning
dc.subjectmodel selection
dc.titleMeta-Learning and the Full Model Selection Problem
dc.typeThesis
thesis.degree.grantorUniversity of Waikato
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (PhD)
dc.date.updated2014-01-24T08:59:42Z
pubs.place-of-publicationHamilton, New Zealanden_NZ


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