Package weka.classifiers.misc
Class VFI
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.misc.VFI
- All Implemented Interfaces:
Serializable
,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:
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 = 0.6)
- Version:
- $Revision: 7180 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the tip text for this propertyvoid
buildClassifier
(Instances instances) Generates the classifier.double[]
distributionForInstance
(Instance instance) Classifies the given test instance.double
getBias()
Get the value of the bias parameterReturns default capabilities of the classifier.String[]
Gets the current settings of VFIReturns the revision string.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
Get whether feature intervals are being weighted by confidenceReturns a string describing this search methodReturns an enumeration describing the available options.static void
Main method for testing this class.void
setBias
(double b) Set the value of the exponential bias towards more confident intervalsvoid
setOptions
(String[] options) Parses a given list of options.void
setWeightByConfidence
(boolean c) Set weighting by confidencetoString()
Returns a description of this classifier.Returns the tip text for this propertyMethods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Constructor Details
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VFI
public VFI()
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Method Details
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globalInfo
Returns a string describing this search method- Returns:
- a description of the search method suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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setOptions
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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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weightByConfidenceTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setWeightByConfidence
public void setWeightByConfidence(boolean c) Set weighting by confidence- Parameters:
c
- true if feature intervals are to be weighted by confidence
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getWeightByConfidence
public boolean getWeightByConfidence()Get whether feature intervals are being weighted by confidence- Returns:
- true if weighting by confidence is selected
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biasTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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
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getBias
public double getBias()Get the value of the bias parameter- Returns:
- the bias parameter
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getOptions
Gets the current settings of VFI- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions()
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
-
buildClassifier
Generates the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- set of instances serving as training data- Throws:
Exception
- if the classifier has not been generated successfully
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toString
Returns a description of this classifier. -
distributionForInstance
Classifies the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- the predicted class for the instance
- Throws:
Exception
- if the instance can't be classified
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
args
- should contain command line arguments for evaluation (see Evaluation).
-