Synopsis#
CC in a trellis structure (rather than a cascaded chain). You set the width and type/connectivity of the trellis, and optionally change the payoff function which guides the placement of nodes (labels) within the trellis.
BibTeX#
@article{JesseRead2015,
author = {Jesse Read, Luca Martino, David Luengo, Pablo Olmos},
journal = {Pattern Recognition},
title = {Scalable multi-output label prediction: From classifier chains to classifier trellises},
year = {2015},
URL = {http://www.sciencedirect.com/science/article/pii/S0031320315000084}
}
Options#
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-H <value>
Determines the width of the trellis (use 0 for chain; use -1 for a square trellis, i.e., width of sqrt(number of labels)).
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-L <value>
Determines the neighbourhood density (the number of neighbours for each node in the trellis). Default = 1, BR = 0.
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-X <value>
The dependency heuristic to use in rearranging the trellis (applicable if chain iterations > 0), default: Ibf (Mutual Information, fast binary version for multi-label data)
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-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
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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
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-S <value>
The seed value for randomizing the data. (default: 0)
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-W <classifier name>
Full name of base classifier. (default: weka.classifiers.trees.J48)
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-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:
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-U
Use unpruned tree.
-
-O
Do not collapse tree.
-
-C <pruning confidence>
Set confidence threshold for pruning. (default 0.25)
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-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)
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-B
Use binary splits only.
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-S
Do not perform subtree raising.
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-L
Do not clean up after the tree has been built.
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-A
Laplace smoothing for predicted probabilities.
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-J
Do not use MDL correction for info gain on numeric attributes.
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-Q <seed>
Seed for random data shuffling (default 1).
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-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).