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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 String globalInfo()
listOptions
public Enumeration listOptions()
setOptions
public void setOptions(String options[]) throws Exception
Don't weight voting intervals by confidence.
Set exponential bias towards confident intervals. default = 1.0
weightByConfidenceTipText
public String weightByConfidenceTipText()
setWeightByConfidence
public void setWeightByConfidence(boolean c)
getWeightByConfidence
public boolean getWeightByConfidence()
biasTipText
public String biasTipText()
setBias
public void setBias(double b)
getBias
public double getBias()
getOptions
public String[] getOptions()
buildClassifier
public void buildClassifier(Instances instances) throws Exception
toString
public String toString()
distributionForInstance
public double[] distributionForInstance(Instance instance) throws Exception
main
public static void main(String args[])
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