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Random forest based similarity measure

Uroš Kosič (2012) Random forest based similarity measure. EngD thesis.

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    We evaluate a similarity measure based on random forests. Existing similarity measure classifies examples with trees in the forest and is based only on instance coocurance in the leaves. The proposed measure takes also nodes on the path to the leaf into account. We present results of clustering and outlier detection on some real data sets. The existing similarity measure works better with different clustering algorithms than extended one. In outlier detection we get better results with extended measure. Because of the evaluation method results cannot be generalized to different approaches to outlier detection.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, random forests, similarity measures, proximity matrix, clustering, outlier detection
    Number of Pages: 69
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Marko Robnik Šikonja276Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=00009495124)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 1904
    Date Deposited: 23 Oct 2012 13:31
    Last Modified: 08 Nov 2012 13:34
    URI: http://eprints.fri.uni-lj.si/id/eprint/1904

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