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A review and comparison of multi-target classification techniques

Miran Levar (2012) A review and comparison of multi-target classification techniques. EngD thesis.

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    Does simultaneous classification of multiple target variables perform better than building a classifier for each of the target variables independently? To answer this question we implemented a set of classification techniques for multi-target classification and integrated them into Orange Multitarget, an add-on for Orange, an open-source machine learning framework. Performance of both multi-target (clustering trees, neural networks, PLS) and single-target techniques (e.g. random forests) was tested on multiple datasets, which included datasets with binary class variables and datasets with multinomial class variables. The results do not show an advantage for either of the techniques. We have also observed that increased correlation between class variables does not increase the performance of multi-target techniques when compared to single-target techniques.

    Item Type: Thesis (EngD thesis)
    Keywords: multi-target classification, clustering trees, evaluation, machine learning
    Number of Pages: 74
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Blaž Zupan106Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=00009482836)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 1820
    Date Deposited: 19 Sep 2012 16:23
    Last Modified: 05 Nov 2012 09:33
    URI: http://eprints.fri.uni-lj.si/id/eprint/1820

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