weka.classifiers.misc
Class VFI

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.misc.VFI
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class VFI
extends Classifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler

Classification by voting feature intervals. Intervals are constucted around each class for each attribute (basically discretization). Class counts are recorded for each interval on each attribute. Classification is by voting. For more info see:

G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997.

Have added a simple attribute weighting scheme. Higher weight is assigned to more confident intervals, where confidence is a function of entropy:
weight (att_i) = (entropy of class distrib att_i / max uncertainty)^-bias

BibTeX:

 @inproceedings{Demiroz1997,
    author = {G. Demiroz and A. Guvenir},
    booktitle = {9th European Conference on Machine Learning},
    pages = {85-92},
    publisher = {Springer},
    title = {Classification by voting feature intervals},
    year = {1997}
 }
 

Faster than NaiveBayes but slower than HyperPipes.

  Confidence: 0.01 (two tailed)

 Dataset                   (1) VFI '-B  | (2) Hyper (3) Naive
                         ------------------------------------
 anneal.ORIG               (10)   74.56 |   97.88 v   74.77
 anneal                    (10)   71.83 |   97.88 v   86.51 v
 audiology                 (10)   51.69 |   66.26 v   72.25 v
 autos                     (10)   57.63 |   62.79 v   57.76
 balance-scale             (10)   68.72 |   46.08 *   90.5  v
 breast-cancer             (10)   67.25 |   69.84 v   73.12 v
 wisconsin-breast-cancer   (10)   95.72 |   88.31 *   96.05 v
 horse-colic.ORIG          (10)   66.13 |   70.41 v   66.12
 horse-colic               (10)   78.36 |   62.07 *   78.28
 credit-rating             (10)   85.17 |   44.58 *   77.84 *
 german_credit             (10)   70.81 |   69.89 *   74.98 v
 pima_diabetes             (10)   62.13 |   65.47 v   75.73 v
 Glass                     (10)   56.82 |   50.19 *   47.43 *
 cleveland-14-heart-diseas (10)   80.01 |   55.18 *   83.83 v
 hungarian-14-heart-diseas (10)   82.8  |   65.55 *   84.37 v
 heart-statlog             (10)   79.37 |   55.56 *   84.37 v
 hepatitis                 (10)   83.78 |   63.73 *   83.87
 hypothyroid               (10)   92.64 |   93.33 v   95.29 v
 ionosphere                (10)   94.16 |   35.9  *   82.6  *
 iris                      (10)   96.2  |   91.47 *   95.27 *
 kr-vs-kp                  (10)   88.22 |   54.1  *   87.84 *
 labor                     (10)   86.73 |   87.67     93.93 v
 lymphography              (10)   78.48 |   58.18 *   83.24 v
 mushroom                  (10)   99.85 |   99.77 *   95.77 *
 primary-tumor             (10)   29    |   24.78 *   49.35 v
 segment                   (10)   77.42 |   75.15 *   80.1  v
 sick                      (10)   65.92 |   93.85 v   92.71 v
 sonar                     (10)   58.02 |   57.17     67.97 v
 soybean                   (10)   86.81 |   86.12 *   92.9  v
 splice                    (10)   88.61 |   41.97 *   95.41 v
 vehicle                   (10)   52.94 |   32.77 *   44.8  *
 vote                      (10)   91.5  |   61.38 *   90.19 *
 vowel                     (10)   57.56 |   36.34 *   62.81 v
 waveform                  (10)   56.33 |   46.11 *   80.02 v
 zoo                       (10)   94.05 |   94.26     95.04 v
                          ------------------------------------
                                (v| |*) |  (9|3|23)  (22|5|8) 
 

Valid options are:

 -C
  Don't weight voting intervals by confidence
 -B <bias>
  Set exponential bias towards confident intervals
  (default = 1.0)

Version:
$Revision: 1.15 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
VFI()
           
 
Method Summary
 java.lang.String biasTipText()
          Returns the tip text for this property
 void buildClassifier(Instances instances)
          Generates the classifier.
 double[] distributionForInstance(Instance instance)
          Classifies the given test instance.
 double getBias()
          Get the value of the bias parameter
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 java.lang.String[] getOptions()
          Gets the current settings of VFI
 java.lang.String getRevision()
          Returns the revision string.
 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.
 boolean getWeightByConfidence()
          Get whether feature intervals are being weighted by confidence
 java.lang.String globalInfo()
          Returns a string describing this search method
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] args)
          Main method for testing this class.
 void setBias(double b)
          Set the value of the exponential bias towards more confident intervals
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setWeightByConfidence(boolean c)
          Set weighting by confidence
 java.lang.String toString()
          Returns a description of this classifier.
 java.lang.String weightByConfidenceTipText()
          Returns the tip text for this property
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

VFI

public VFI()
Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing this search method

Returns:
a description of the search method suitable for displaying in the explorer/experimenter gui

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

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class Classifier
Returns:
an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
Parses a given list of options.

Valid options are:

 -C
  Don't weight voting intervals by confidence
 -B <bias>
  Set exponential bias towards confident intervals
  (default = 1.0)

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

weightByConfidenceTipText

public java.lang.String weightByConfidenceTipText()
Returns the tip text for this property

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

setWeightByConfidence

public void setWeightByConfidence(boolean c)
Set weighting by confidence

Parameters:
c - true if feature intervals are to be weighted by confidence

getWeightByConfidence

public boolean getWeightByConfidence()
Get whether feature intervals are being weighted by confidence

Returns:
true if weighting by confidence is selected

biasTipText

public java.lang.String biasTipText()
Returns the tip text for this property

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

setBias

public void setBias(double b)
Set the value of the exponential bias towards more confident intervals

Parameters:
b - the value of the bias parameter

getBias

public double getBias()
Get the value of the bias parameter

Returns:
the bias parameter

getOptions

public java.lang.String[] getOptions()
Gets the current settings of VFI

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

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 instances)
                     throws java.lang.Exception
Generates the classifier.

Specified by:
buildClassifier in class Classifier
Parameters:
instances - set of instances serving as training data
Throws:
java.lang.Exception - if the classifier has not been generated successfully

toString

public java.lang.String toString()
Returns a description of this classifier.

Overrides:
toString in class java.lang.Object
Returns:
a description of this classifier as a string.

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
Classifies the given test instance.

Overrides:
distributionForInstance in class Classifier
Parameters:
instance - the instance to be classified
Returns:
the predicted class for the instance
Throws:
java.lang.Exception - if the instance can't be classified

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).