Synopsis#
PLST - Principle Label Space Transformation. Uses SVD to generate a matrix that transforms the label space. This implementation is adapted from the MatLab implementation provided by the authors.
https://github.com/hsuantien/mlc_lsdr
For more information see: Farbound Tai, Hsuan-Tien Lin: Multilabel classification with principal label space transformation. In: Neural Computation, 2508-2542, 2012.
BibTeX#
@inproceedings{Tai2012,
author = {Farbound Tai and Hsuan-Tien Lin},
booktitle = {Neural Computation},
number = {9},
pages = {2508-2542},
title = {Multilabel classification with principal label space transformation},
volume = {24},
year = {2012}
}
Options#
-
-size <value>
Size of the compressed matrix. Should be less than the number of labels and more than 1. (default: 3)
-
-W <classifier name>
Full name of base classifier. (default: meka.classifiers.multitarget.CR)
-
-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 meka.classifiers.multitarget.CR:
-
-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.functions.LinearRegression:
-
-S <number of selection method>
Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
-
-C
Do not try to eliminate colinear attributes.
-
-R <double>
Set ridge parameter (default 1.0e-8).
-
-minimal
Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)
-
-additional-stats
Output additional statistics.
-
-use-qr
Use QR decomposition to find coefficients
-
-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 4).
-
-batch-size
The desired batch size for batch prediction (default 100).