MLCBMaD (meka 1.9.1 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - meka.classifiers.multilabel.LabelTransformationClassifier
    - - meka.classifiers.multilabel.MLCBMaD

All Implemented Interfaces:

Serializable, Cloneable, MultiLabelClassifier, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler
```
public class MLCBMaD
extends LabelTransformationClassifier
implements weka.core.TechnicalInformationHandler
```
MLC-BMaD - Multi-Label Classification using Boolean Matrix Decomposition. Transforms the labels using a Boolean matrix decomposition, the first resulting matrix are used as latent labels and a classifier is trained to predict them. The second matrix is used in a multiplication to decompress the predicted latent labels.
See: Jörg Wicker, Bernhard Pfahringer, Stefan Kramer. Multi-label Classification Using Boolean Matrix Decomposition. Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186, ACM, 2012.

Author:

Joerg Wicker (jw@oerg-wicker.org)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected weka.core.Instances`	`compressedMatrix` The compressed matrix.
`protected static long`	`serialVersionUID`
`protected int`	`size` The size of the compressed matrix, i.e., the number of columns of Y'.
`protected double`	`threshold` The threshold t of the decomposition process, see the paper for details.
`protected weka.core.Instances`	`uppermatrix` The upper matrix.

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

MLCBMaD()

Constructors
Constructor and Description
`MLCBMaD()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected int`	`getDefaultSize()` Default size = 20, seems to be a good choice for most data sets.
`protected double`	`getDefaultThreshold()` Default threshold = 0.5, has to be in at least half of the rows.
`String`	`getModel()` Returns a string representation of the model.
`String[]`	`getOptions()` Returns an array with the options of the classifier.
`int`	`getSize()` Returns the size of the compressed labels.
`weka.core.TechnicalInformation`	`getTechnicalInformation()`
`double`	`getThreshold()` Getter for the threshold for Boolean matrix decomposition.
`String`	`globalInfo()` Returns the global information of the classifier.
`Enumeration`	`listOptions()` Returns an enumeration of the options.
`static void`	`main(String[] args)` Main method for testing.
`void`	`setOptions(String[] options)` Sets the options to the given values in the array.
`void`	`setSize(int size)` Sets the size of the compressed labels.
`void`	`setThreshold(double threshold)` Sets the threshold for the Boolean matrix decomposition.
`String`	`sizeTipText()` The tooltip for the size.
`String`	`thresholdTipText()` Tooltip for the threshold.
`String`	`toString()`
`weka.core.Instance`	`transformInstance(weka.core.Instance x)` Transforms the instance in the prediction process before given to the internal multi-label or multi-target classifier.
`weka.core.Instances`	`transformLabels(weka.core.Instances D)` The method to transform the labels into another set of latent labels, typically a compression method is used, e.g., Boolean matrix decomposition in the case of MLC-BMaD, or matrix multiplication based on SVD for PLST.
`double[]`	`transformPredictionsBack(double[] y)` Transforms the predictions of the internal classifier back to the original labels.

Methods inherited from class meka.classifiers.multilabel.LabelTransformationClassifier
buildClassifier, defaultClassifierString, distributionForInstance, extractPart, getDefaultClassifier, getRevision, setClassifier, testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getCapabilities, getClassifier, getClassifierSpec, postExecution, preExecution

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier
debugTipText, getDebug, setDebug

Methods inherited from interface weka.classifiers.Classifier
classifyInstance, getCapabilities

- Field Detail
  - serialVersionUID
```
protected static final long serialVersionUID
```
    See Also:
    
    Constant Field Values
  - uppermatrix
```
protected weka.core.Instances uppermatrix
```
    The upper matrix. Decomposition is done such that Y=Y'*M, this is M.
  - compressedMatrix
```
protected weka.core.Instances compressedMatrix
```
    The compressed matrix. Decomposition is done such that Y=Y'*M, this is Y'.
  - size
```
protected int size
```
    The size of the compressed matrix, i.e., the number of columns of Y'.
  - threshold
```
protected double threshold
```
    The threshold t of the decomposition process, see the paper for details. Sets the minimum frequency to be considered a frequent coocurence. Between 0 (all are frequent) and 1 (must be in all rows to be frequent).
- Constructor Detail
  - MLCBMaD
```
public MLCBMaD()
```
- Method Detail
  - getDefaultThreshold
```
protected double getDefaultThreshold()
```
    Default threshold = 0.5, has to be in at least half of the rows.
    
    Returns:
    
    the default threshold.
  - getDefaultSize
```
protected int getDefaultSize()
```
    Default size = 20, seems to be a good choice for most data sets.
    
    Returns:
    
    the tooltip
  - getSize
```
public int getSize()
```
    Returns the size of the compressed labels.
    
    Returns:
    
    The size of the compressed labels, i.e., the number of columns.
  - setSize
```
public void setSize(int size)
```
    Sets the size of the compressed labels.
    
    Parameters:
    
    size - The size of the compressed labels, i.e., the number of columns.
  - sizeTipText
```
public String sizeTipText()
```
    The tooltip for the size.
  - getThreshold
```
public double getThreshold()
```
    Getter for the threshold for Boolean matrix decomposition.
    
    Returns:
    
    the threshold for the Boolean matrix decomposition.
  - setThreshold
```
public void setThreshold(double threshold)
```
    Sets the threshold for the Boolean matrix decomposition.
    
    Parameters:
    
    threshold - the threshold for the Boolean matrix decomposition.
  - thresholdTipText
```
public String thresholdTipText()
```
    Tooltip for the threshold.
    
    Returns:
    
    Description of the threshold for Boolean matrix decomposition.
  - globalInfo
```
public String globalInfo()
```
    Returns the global information of the classifier.
    
    Returns:
    
    Global information of the classfier
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration of the options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    Enumeration of the options.
  - getOptions
```
public String[] getOptions()
```
    Returns an array with the options of the classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    Array of options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Sets the options to the given values in the array.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Parameters:
    
    options - The options to be set.
    
    Throws:
    
    Exception
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Specified by:
    
    getTechnicalInformation in interface weka.core.TechnicalInformationHandler
  - transformInstance
```
public weka.core.Instance transformInstance(weka.core.Instance x)
                                     throws Exception
```
    Description copied from class: LabelTransformationClassifier
    
    Transforms the instance in the prediction process before given to the internal multi-label or multi-target classifier. The instance is passed having the original set of labels, these must be replaced with the transformed labels (attributes) so that the internla classifier can predict them.
    
    Specified by:
    
    transformInstance in class LabelTransformationClassifier
    
    Parameters:
    
    x - The instance to transform. Consists of features and labels.
    
    Returns:
    
    The transformed instance. Consists of features and transformed labels.
    
    Throws:
    
    Exception
  - transformLabels
```
public weka.core.Instances transformLabels(weka.core.Instances D)
                                    throws Exception
```
    Description copied from class: LabelTransformationClassifier
    
    The method to transform the labels into another set of latent labels, typically a compression method is used, e.g., Boolean matrix decomposition in the case of MLC-BMaD, or matrix multiplication based on SVD for PLST.
    
    Specified by:
    
    transformLabels in class LabelTransformationClassifier
    
    Parameters:
    
    D - the instances to transform into new instances with transformed labels. The Instances consist of features and original labels.
    
    Returns:
    
    The resulting instances. Instances consist of features and transformed labels.
    
    Throws:
    
    Exception
  - transformPredictionsBack
```
public double[] transformPredictionsBack(double[] y)
```
    Description copied from class: LabelTransformationClassifier
    
    Transforms the predictions of the internal classifier back to the original labels.
    
    Specified by:
    
    transformPredictionsBack in class LabelTransformationClassifier
    
    Parameters:
    
    y - The predictions that should be transformed back. The array consists only of the predictions as they are returned from the internal classifier.
    
    Returns:
    
    The transformed predictions.
  - getModel
```
public String getModel()
```
    Description copied from interface: MultiLabelClassifier
    
    Returns a string representation of the model.
    
    Specified by:
    
    getModel in interface MultiLabelClassifier
    
    Returns:
    
    the model
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - main
```
public static void main(String[] args)
                 throws Exception
```
    Main method for testing.
    
    Parameters:
    
    args - - Arguments passed from the command line
    
    Throws:
    
    Exception

Class MLCBMaD

Field Summary

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class meka.classifiers.multilabel.LabelTransformationClassifier

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier

Methods inherited from interface weka.classifiers.Classifier

Field Detail

serialVersionUID

uppermatrix

compressedMatrix

size

threshold

Constructor Detail

MLCBMaD

Method Detail

getDefaultThreshold

getDefaultSize

getSize

setSize

sizeTipText

getThreshold

setThreshold

thresholdTipText

globalInfo

listOptions

getOptions

setOptions

getTechnicalInformation

transformInstance

transformLabels

transformPredictionsBack

getModel

toString

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