public class VFI extends Classifier implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler
@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 = 0.6)
Constructor and Description |
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VFI() |
Modifier and Type | Method and Description |
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java.lang.String |
biasTipText()
Returns the tip text for this property
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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.
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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
|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-C Don't weight voting intervals by confidence
-B <bias> Set exponential bias towards confident intervals (default = 1.0)
setOptions
in interface OptionHandler
setOptions
in class Classifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String weightByConfidenceTipText()
public void setWeightByConfidence(boolean c)
c
- true if feature intervals are to be weighted by confidencepublic boolean getWeightByConfidence()
public java.lang.String biasTipText()
public void setBias(double b)
b
- the value of the bias parameterpublic double getBias()
public java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class Classifier
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in class Classifier
instances
- set of instances serving as training datajava.lang.Exception
- if the classifier has not been generated successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if the instance can't be classifiedpublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] args)
args
- should contain command line arguments for evaluation
(see Evaluation).