Synopsis

PLST - Principle Label Space Transformation. Uses SVD to generate a matrix that transforms the label space. This implementation is adapted from the MatLab implementation provided by the authors.

https://github.com/hsuantien/mlc_lsdr

For more information see: Farbound Tai, Hsuan-Tien Lin: Multilabel classification with principal label space transformation. In: Neural Computation, 2508-2542, 2012.

BibTeX

@inproceedings{Tai2012,
   author = {Farbound Tai and Hsuan-Tien Lin},
   booktitle = {Neural Computation},
   number = {9},
   pages = {2508-2542},
   title = {Multilabel classification with principal label space transformation},
   volume = {24},
   year = {2012}
}

Options

  • -size <value>

    Size of the compressed matrix. Should be less than the number of labels and more than 1. (default: 3)

  • -W <classifier name>

    Full name of base classifier. (default: meka.classifiers.multitarget.CR)

  • -output-debug-info

    If set, classifier is run in debug mode and may output additional info to the console

  • -do-not-check-capabilities

    If set, classifier capabilities are not checked before classifier is built (use with caution).

  • -num-decimal-places

    The number of decimal places for the output of numbers in the model (default 2).

  • -batch-size

    The desired batch size for batch prediction (default 100).

Options specific to classifier meka.classifiers.multitarget.CR:

  • -W <classifier name>

    Full name of base classifier. (default: weka.classifiers.trees.J48)

  • -output-debug-info

    If set, classifier is run in debug mode and may output additional info to the console

  • -do-not-check-capabilities

    If set, classifier capabilities are not checked before classifier is built (use with caution).

  • -num-decimal-places

    The number of decimal places for the output of numbers in the model (default 2).

  • -batch-size

    The desired batch size for batch prediction (default 100).

Options specific to classifier weka.classifiers.functions.LinearRegression:

  • -S <number of selection method>

    Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)

  • -C

    Do not try to eliminate colinear attributes.

  • -R <double>

    Set ridge parameter (default 1.0e-8).

  • -minimal

    Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)

  • -additional-stats

    Output additional statistics.

  • -output-debug-info

    If set, classifier is run in debug mode and may output additional info to the console

  • -do-not-check-capabilities

    If set, classifier capabilities are not checked before classifier is built (use with caution).

  • -num-decimal-places

    The number of decimal places for the output of numbers in the model (default 4).

  • -batch-size

    The desired batch size for batch prediction (default 100).