FLR

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## weka.classifiers.misc
Class FLR

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.misc.FLR

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, Summarizable, TechnicalInformationHandler

    public class FLR

extends Classifier
implements java.io.Serializable, Summarizable, AdditionalMeasureProducer, TechnicalInformationHandler

Fuzzy Lattice Reasoning Classifier (FLR) v5.0
The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment.
The current version can be used for classification using numeric predictors.
For more information see:
I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003.
V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

BibTeX:

     @inproceedings{Athanasiadis2003,
        address = {Gdansk, Poland},
        author = {I. N. Athanasiadis and V. G. Kaburlasos and P. A. Mitkas and V. Petridis},
        booktitle = {1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003)},
        note = {Abstract in ICSC-NAISO Academic Press, Canada (ISBN:3906454339), pg.51},
        publisher = {ICSC-NAISO Academic Press},
        title = {Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support},
        year = {2003}
     }

     @unpublished{Kaburlasos2003,
        author = {V. G. Kaburlasos and I. N. Athanasiadis and P. A. Mitkas and V. Petridis},
        title = {Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration},
        year = {2003}
     }

Valid options are:

     -R
      Set vigilance parameter rhoa.
      (a float in range [0,1])

     -B
      Set boundaries File
      Note:  The boundaries file is a simple text file containing
      a row with a Fuzzy Lattice defining the metric space.
      For example, the boundaries file could contain the following
      the metric space for the iris dataset:
      [ 4.3  7.9 ]  [ 2.0  4.4 ]  [ 1.0  6.9 ]  [ 0.1  2.5 ]  in Class:  -1
      This lattice just contains the min and max value in each
      dimension.
      In other kind of problems this may not be just a min-max
      operation, but it could contain limits defined by the problem
      itself.
      Thus, this option should be set by the user.
      If ommited, the metric space used contains the mins and maxs
      of the training split.

     -Y
      Show Rules

For further information contact I.N.Athanasiadis (ionathan@iti.gr)

Version:
  5.0, $Revision: 1.10 $
Author:
  Ioannis N. Athanasiadis (email: ionathan@iti.gr, alias: ionathan@ieee.org)
See Also:
  Serialized Form

Field Summary
`static float` | `EPSILON`

Constructor Summary
`FLR()`

Method Summary
`java.lang.String` | `boundsFileTipText()` Returns the tip text for this property
`void` | `buildClassifier(Instances data)` Builds the FLR Classifier
`void` | `checkBounds()` Checks the metric space
`double` | `classifyInstance(Instance instance)` Classifies a given instance using the FLR Classifier model
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String` | `getBoundsFile()` Get boundaries File
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `getRhoa()` Get rhoa
`boolean` | `getShowRules()` Get ShowRules parameter
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String` | `globalInfo()` Returns a description of the classifier suitable for displaying in the explorer/experimenter gui
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] args)` Main method for testing this class.
`double` | `measureNumRules()` Additional measure Number of Rules
`java.lang.String` | `rhoaTipText()` Returns the tip text for this property
`void` | `setBounds(Instances data)` Sets the metric space from the training set using the min-max stats, in case -B option is not used.
`void` | `setBoundsFile(java.lang.String newBoundsFile)` Set Boundaries File
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setRhoa(double newRhoa)` Set rhoa
`void` | `setShowRules(boolean flag)` Set ShowRules flag
`java.lang.String` | `showRules()` Returns the induced set of Fuzzy Lattice Rules
`java.lang.String` | `showRulesTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Returns a description of the classifier.
`java.lang.String` | `toSummaryString()` Returns a superconcise version of the model

Methods inherited from class weka.classifiers.Classifier
`debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Field Detail

### EPSILON

    public static final float EPSILON

See Also:
  Constant Field Values

Constructor Detail

### FLR

    public FLR()

Method Detail

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `Classifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

    public void buildClassifier(Instances data)
                         throws java.lang.Exception

  Builds the FLR Classifier

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the training dataset (Instances)
  Throws:
    `java.lang.Exception` - if the training dataset is not supported or is erroneous

### classifyInstance

    public double classifyInstance(Instance instance)

  Classifies a given instance using the FLR Classifier model

  Overrides:
    `classifyInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    the class index into which the instance is classfied

### toString

    public java.lang.String toString()

  Returns a description of the classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    String describing the FLR model

### toSummaryString

    public java.lang.String toSummaryString()

  Returns a superconcise version of the model

  Specified by:
    `toSummaryString` in interface `Summarizable`
  Returns:
    String descibing the FLR model very shortly

### showRules

    public java.lang.String showRules()

  Returns the induced set of Fuzzy Lattice Rules

  Returns:
    String containing the ruleset

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Valid options are:

       -R
        Set vigilance parameter rhoa.
        (a float in range [0,1])

       -B
        Set boundaries File
        Note:  The boundaries file is a simple text file containing
        a row with a Fuzzy Lattice defining the metric space.
        For example, the boundaries file could contain the following
        the metric space for the iris dataset:
        [ 4.3  7.9 ]  [ 2.0  4.4 ]  [ 1.0  6.9 ]  [ 0.1  2.5 ]  in Class:  -1
        This lattice just contains the min and max value in each
        dimension.
        In other kind of problems this may not be just a min-max
        operation, but it could contain limits defined by the problem
        itself.
        Thus, this option should be set by the user.
        If ommited, the metric space used contains the mins and maxs
        of the training split.

       -Y
        Show Rules

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `Classifier`
  Returns:
    enumeration an enumeration of valid options

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

       -R
        Set vigilance parameter rhoa.
        (a float in range [0,1])

       -B
        Set boundaries File
        Note:  The boundaries file is a simple text file containing
        a row with a Fuzzy Lattice defining the metric space.
        For example, the boundaries file could contain the following
        the metric space for the iris dataset:
        [ 4.3  7.9 ]  [ 2.0  4.4 ]  [ 1.0  6.9 ]  [ 0.1  2.5 ]  in Class:  -1
        This lattice just contains the min and max value in each
        dimension.
        In other kind of problems this may not be just a min-max
        operation, but it could contain limits defined by the problem
        itself.
        Thus, this option should be set by the user.
        If ommited, the metric space used contains the mins and maxs
        of the training split.

       -Y
        Show Rules

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `Classifier`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported (

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `Classifier`
  Returns:
    an array of strings suitable for passing to setOptions

### getRhoa

    public double getRhoa()

  Get rhoa

  Returns:
    the value of this parameter

### getBoundsFile

    public java.lang.String getBoundsFile()

  Get boundaries File

  Returns:
    the value of this parameter

### getShowRules

    public boolean getShowRules()

  Get ShowRules parameter

  Returns:
    the value of this parameter

### setRhoa

    public void setRhoa(double newRhoa)
                 throws java.lang.Exception

  Set rhoa

  Parameters:
    `newRhoa` - sets the rhoa value
  Throws:
    `java.lang.Exception` - if rhoa is not in range [0,1]

### setBoundsFile

    public void setBoundsFile(java.lang.String newBoundsFile)

  Set Boundaries File

  Parameters:
    `newBoundsFile` - a new file containing the boundaries

### setShowRules

    public void setShowRules(boolean flag)

  Set ShowRules flag

  Parameters:
    `flag` - the new value of this parameter

### setBounds

    public void setBounds(Instances data)

  Sets the metric space from the training set using the min-max stats, in case -B option is not used.

  Parameters:
    `data` - is the training set

### checkBounds

    public void checkBounds()

  Checks the metric space

### rhoaTipText

    public java.lang.String rhoaTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### boundsFileTipText

    public java.lang.String boundsFileTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### showRulesTipText

    public java.lang.String showRulesTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure

  Specified by:
    `getMeasure` in interface `AdditionalMeasureProducer`
  Parameters:
    `additionalMeasureName` - the name of the measure to query for its value
  Returns:
    the value of the named measure
  Throws:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of the additional measure names

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### measureNumRules

    public double measureNumRules()

  Additional measure Number of Rules

  Returns:
    the number of rules induced

### globalInfo

    public java.lang.String globalInfo()

  Returns a description of the classifier suitable for displaying in the explorer/experimenter gui

  Returns:
    the description

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] args)

  Main method for testing this class.

  Parameters:
    `args` - should contain command line arguments for evaluation (see Evaluation).

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