Synopsis#

Classifier Chains with Monte Carlo optimization. For more information see: Jesse Read, Luca Martino, David Luengo: Efficient Monte Carlo Optimization for Multi-label Classifier Chains. In: ICASSP'13: International Conference on Acoustics, Speech, and Signal Processing, 2013.

Jesse Read, Luca Martino, David Luengo (2013). Efficient Monte Carlo Optimization for Multi-dimensional Classifier Chains. Elsevier Pattern Recognition..

BibTeX#

@inproceedings{Read2013,
   author = {Jesse Read and Luca Martino and David Luengo},
   booktitle = {ICASSP'13: International Conference on Acoustics, Speech, and Signal Processing},
   title = {Efficient Monte Carlo Optimization for Multi-label Classifier Chains},
   year = {2013}
}

@article{Read2013,
   author = {Jesse Read and Luca Martino and David Luengo},
   journal = {Elsevier Pattern Recognition},
   title = {Efficient Monte Carlo Optimization for Multi-dimensional Classifier Chains},
   year = {2013}
}

Options#

• -Is <value>

  The number of iterations to search the chain space at train time. default: 0

• -Iy <value>

  The number of iterations to search the output space at test time. default: 10

• -P <value>

  Sets the payoff function. Any of those listed in regular evaluation output will do (e.g., 'Exact match'). default: Exact match

• -S <value>

  The seed value for randomizing the data. (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).