Synopsis#
Takes RAndom partition of labELs; like RAkEL but labelsets are disjoint / non-overlapping subsets.
BibTeX#
@article{GrigoriosTsoumakas2014,
author = {Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas},
booktitle = {ICDM'14: International Conference on Data Mining (ICDM 2014). Shenzen, China.},
journal = {IEEE Transactions on Knowledge and Data Engineering},
number = {7},
pages = {941--946},
title = {Random k-Labelsets for Multi-Label Classification},
volume = {23},
year = {2014}
}
@inproceedings{missing_id,
author = {Jesse Read, Antti Puurula, Albert Bifet},
title = {Classifier Chains for Multi-label Classification}
}
Options#
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-k <num>
The number of labels in each partition -- should be 1 <= k < (L/2) where L is the total number of labels.
-
-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.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.
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-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).