Synopsis#

Updateable PS

BibTeX#

@inproceedings{JesseRead2008,
   author = {Jesse Read, Bernhard Pfahringer, Geoff Holmes},
   booktitle = {ICDM'08: International Conference on Data Mining (ICDM 2008). Pisa, Italy.},
   title = {Multi-label Classification Using Ensembles of Pruned Sets},
   year = {2008}
}

Options#

• -I <value>

  Sets the buffer size default: 1000

• -support <value>

  Sets the max. num. of combs. default: 10

• -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.HoeffdingTree)

• -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.HoeffdingTree:

• -L

  The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive. (default = 2)

• -S

  The splitting criterion to use. 0 = Gini, 1 = Info gain (default = 1)

• -E

  The allowable error in a split decision - values closer to zero will take longer to decide (default = 1e-7)

• -H

  Threshold below which a split will be forced to break ties (default = 0.05)

• -M

  Minimum fraction of weight required down at least two branches for info gain splitting (default = 0.01)

• -G

  Grace period - the number of instances a leaf should observe between split attempts (default = 200)

• -N

  The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only) (default = 0)

• -P

  Print leaf models when using naive Bayes at the leaves.