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).