VFI (Documentation for extended WEKA including Ensembles of Hierarchically Nested Dichotomies)

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weka.classifiers.misc
Class VFI

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

All Implemented Interfaces:: java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class VFI
extends Classifier
implements OptionHandler, WeightedInstancesHandler

Class implementing the voting feature interval classifier. For numeric attributes, upper and lower boundaries (intervals) are constructed around each class. Discrete attributes have point intervals. Class counts are recorded for each interval on each feature. Classification is by voting. Missing values are ignored. Does not handle numeric class.

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.

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)

For more information, see

Demiroz, G. and Guvenir, A. (1997) "Classification by voting feature intervals", ECML-97.

Valid options are:

-C
Don't Weight voting intervals by confidence.

-B
Set exponential bias towards confident intervals. default = 1.0

Version:: $Revision: 1.9 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary
`protected double`	`m_bias` Bias towards more confident intervals
`protected int`	`m_ClassIndex` The index of the class attribute
`protected double[][][]`	`m_counts` The class counts for each interval of each attribute
`protected double[]`	`m_globalCounts` The global class counts
`protected Instances`	`m_Instances` The training data
`protected double[][]`	`m_intervalBounds` The lower bounds for each attribute
`protected double`	`m_maxEntrop` The maximum entropy for the class
`protected int`	`m_NumClasses` The number of classes
`protected boolean`	`m_weightByConfidence` Exponentially bias more confident intervals
`private double`	`TINY`

Fields inherited from class weka.classifiers.Classifier

m_Debug

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
`java.lang.String[]`	`getOptions()` Gets the current settings of VFI
`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, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_ClassIndex

protected int m_ClassIndex

The index of the class attribute

m_NumClasses

protected int m_NumClasses

The number of classes

m_Instances

protected Instances m_Instances

The training data

m_counts

protected double[][][] m_counts

The class counts for each interval of each attribute

m_globalCounts

protected double[] m_globalCounts

The global class counts

m_intervalBounds

protected double[][] m_intervalBounds

The lower bounds for each attribute

m_maxEntrop

protected double m_maxEntrop

The maximum entropy for the class

m_weightByConfidence

protected boolean m_weightByConfidence

Exponentially bias more confident intervals

m_bias

protected double m_bias

Bias towards more confident intervals

TINY

private double TINY

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

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
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()