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#
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-Is <value>
The number of iterations to search the chain space at train time. default: 0
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-Iy <value>
The number of iterations to search the output space at test time. default: 10
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-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)
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-R
Use reduced error pruning.
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-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.
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-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).