Synopsis

Takes RAndom partition of labELs; like RAkEL but labelsets are disjoint / non-overlapping subsets.

BibTeX

@article{GrigoriosTsoumakas2014,
   author = {Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas},
   booktitle = {ICDM'14: International Conference on Data Mining (ICDM 2014). Shenzen, China.},
   journal = {IEEE Transactions on Knowledge and Data Engineering},
   number = {7},
   pages = {941--946},
   title = {Random k-Labelsets for Multi-Label Classification},
   volume = {23},
   year = {2014}
}

@inproceedings{missing_id,
   author = {Jesse Read, Antti Puurula, Albert Bifet},
   title = {Classifier Chains for Multi-label Classification}
}

Options

  • -k <num>

    The number of labels in each partition -- should be 1 <= k < (L/2) where L is the total number of labels.

  • -P <value>

    Sets the pruning value, defining an infrequent labelset as one which occurs <= P times in the data (P = 0 defaults to LC). default: 0 (LC)

  • -N <value>

    Sets the (maximum) number of frequent labelsets to subsample from the infrequent labelsets. default: 0 (none) n N = n -n N = n, or 0 if LCard(D) >= 2 n-m N = random(n,m)

  • -S <value>

    The seed value for randomization default: 0

  • -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.trees.J48:

  • -U

    Use unpruned tree.

  • -O

    Do not collapse tree.

  • -C <pruning confidence>

    Set confidence threshold for pruning. (default 0.25)

  • -M <minimum number of instances>

    Set minimum number of instances per leaf. (default 2)

  • -R

    Use reduced error pruning.

  • -N <number of folds>

    Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

  • -B

    Use binary splits only.

  • -S

    Do not perform subtree raising.

  • -L

    Do not clean up after the tree has been built.

  • -A

    Laplace smoothing for predicted probabilities.

  • -J

    Do not use MDL correction for info gain on numeric attributes.

  • -Q <seed>

    Seed for random data shuffling (default 1).

  • -doNotMakeSplitPointActualValue

    Do not make split point actual value.

  • -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).