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java.lang.Object | +----weka.classifiers.Classifier | +----weka.classifiers.DistributionClassifier | +----weka.classifiers.VFI
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
-C
-B
Set exponential bias towards confident intervals. default = 1.0
VFI()
biasTipText()
buildClassifier(Instances)
distributionForInstance(Instance)
getBias()
getOptions()
getWeightByConfidence()
globalInfo()
listOptions()
main(String[])
setBias(double)
setOptions(String[])
setWeightByConfidence(boolean)
toString()
weightByConfidenceTipText()
VFI
public VFI()
globalInfo
public java.lang.String globalInfo()
Returns a string describing this search method
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
Parses a given list of options. Valid options are:
Don't weight voting intervals by confidence.
Set exponential bias towards confident intervals. default = 1.0
options
- the list of options as an array of strings
weightByConfidenceTipText
public java.lang.String weightByConfidenceTipText()
Returns the tip text for this property
setWeightByConfidence
public void setWeightByConfidence(boolean c)
Set weighting by confidence
c
- true if feature intervals are to be weighted by confidence
getWeightByConfidence
public boolean getWeightByConfidence()
Get whether feature intervals are being weighted by confidence
biasTipText
public java.lang.String biasTipText()
Returns the tip text for this property
setBias
public void setBias(double b)
Set the value of the exponential bias towards more confident intervals
b
- the value of the bias parameter
getBias
public double getBias()
Get the value of the bias parameter
getOptions
public java.lang.String[] getOptions()
Gets the current settings of VFI
buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Generates the classifier.
instances
- set of instances serving as training data
toString
public java.lang.String toString()
Returns a description of this classifier.
distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Classifies the given test instance.
instance
- the instance to be classified
main
public static void main(java.lang.String args[])
Main method for testing this class.
args
- should contain command line arguments for evaluation
(see Evaluation).
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