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Created-By: 1.8.0_91-b15 (Oracle Corporation)
Class-Path: lib/weka.jar
X-COMMENT: Main-Class will be added automatically by build
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ConstantValueÛB ~:pøx m_coveragePercent F m_numberSamplesByCoverage I m_trueCoverage D m_bagSizePercentToReduce m_minExamplesPerClassPercent m_stExceptionalSituationsMessage Ljava/lang/String; NumberSamples_FixedValue NumberSamples_BasedOnCoverage TAGS_WAYS_TO_SET_NUMBER_SAMPLES [Lweka/core/Tag; m_RMnumberSamplesHowToSet m_RMnumberSamples m_RMreplacement Z m_RMbagSizePercent m_RMnewDistrMinClass <init> ()V Code LineNumberTable LocalVariableTable this (Lweka/classifiers/trees/J48Consolidated;
globalInfo ()Ljava/lang/String; getTechnicalInformation "()Lweka/core/TechnicalInformation; result Lweka/core/TechnicalInformation;
additional getCapabilities ()Lweka/core/Capabilities; Lweka/core/Capabilities; e Ljava/lang/Exception;
StackMapTableö() buildClassifier (Lweka/core/Instances;)V instances Lweka/core/Instances; modSelection +Lweka/classifiers/trees/j48/ModelSelection;
samplesVector [Lweka/core/Instances;3
Exceptions generateSamples -(Lweka/core/Instances;)[Lweka/core/Instances; random Ljava/util/Random; instancesWMC >Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated; dataSize bagSizec;N generateStratifiedSamples j(Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated;IILjava/util/Random;)[Lweka/core/Instances; bagSizePercent newClassSize iClass
numberSamples coverage iMostDisfavorClass trueCoverageByClass ratioClassDistr
newBagData bagData bagClass iSample
numClasses
classesVector ?[Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated; classSizeVector [I iMinClass newClassSizeVector localBagSize bagBySampleClassRatioVector [D generateFreeDistrSamples bagBySampleRatio $generateSamplesChangingMinClassDistr
distrMinClass classSize i_iMinClass iClassSizeOrdVector iMajClass
isBalanced minExamplesPerClass maxClassSizeVector forceToReduceSamplesSize printSamplesVector ([Lweka/core/Instances;)V listOptions ()Ljava/util/Enumeration; newVector Ljava/util/Vector; en Ljava/util/Enumeration; LocalVariableTypeTable &Ljava/util/Vector<Lweka/core/Option;>;
setOptions ([Ljava/lang/String;)V options [Ljava/lang/String; RMnumberSamplesString RMbagSizePercentString RMnewDistrMinClassString´
getOptions ()[Ljava/lang/String; i &Ljava/util/Vector<Ljava/lang/String;>; toString toStringResamplingMethod st ch_line [C lineµ RMnumberSamplesHowToSetTipText getRMnumberSamplesHowToSet ()Lweka/core/SelectedTag; setRMnumberSamplesHowToSet (Lweka/core/SelectedTag;)V newEvWay newWayToSetNumberSamples Lweka/core/SelectedTag; RMnumberSamplesTipText getRMnumberSamples ()F setRMnumberSamples (F)V v RMreplacementTipText getRMreplacement ()Z setRMreplacement (Z)V (ZZ)V checkComb RMbagSizePercentTipText getRMbagSizePercent ()I setRMbagSizePercent (I)V (IZ)V RMnewDistrMinClassTipText getRMnewDistrMinClass setRMnewDistrMinClass (FZ)V ;checkBagSizePercentAndReplacementAndNewDistrMinClassOptions (ZIF)V replacement newDistrMinClass reducedErrorPruningTipText setReducedErrorPruning numFoldsTipText setNumFolds binarySplitsTipText setBinarySplits seedTipText toSummaryString stNumberSamplesByCoverage measureNumberSamplesByCoverage ()D measureTrueCoverage enumerateMeasures enm measures
getMeasure (Ljava/lang/String;)D additionalMeasureName main argv <clinit>
SourceFile J48Consolidated.java EF 12 78 &weka/classifiers/trees/J48Consolidated ?2 /0 @0 AB C2 D0 java/lang/StringBuilder]Class for generating a pruned or unpruned C45 consolidated tree. Uses the Consolidated Tree Construction (CTC) algorithm: a single tree is built based on a set of subsamples. New options are added to the J48 class to set the Resampling Method (RM) for the generation of samples to be used in the consolidation process.
Recently, a new way has been added to determine the number of samples to be used in the consolidation process which guarantees the minimum percentage, the coverage value, of the examples of the original sample to be contained by the set of built subsamples. For more information, see:
NO ±M weka/core/TechnicalInformation
E cJesús M. Pérez and Javier Muguerza and Olatz Arbelaitz and Ibai Gurrutxaga and José I. MartÃÂn 2007 JCombining multiple class distribution modified subsamples in a single tree Pattern Recognition Letters 28 4 414-422 .http://dx.doi.org/10.1016/j.patrec.2006.08.013 _Igor Ibarguren and Jesús M. Pérez and Javier Muguerza and Ibai Gurrutxaga and Olatz Arbelaitz 2015 FCoverage-based resampling: Building robust consolidated decision trees Knowledge Based Systems 79 51-67 .http://dx.doi.org/10.1016/j.knosys.2014.12.023 5weka/classifiers/trees/j48/C45PruneableClassifierTree B 0 B B B E ST java/lang/Exception weka/core/Capabilities E! "! Dweka/classifiers/trees/j48Consolidated/C45ConsolidatedModelSelection #2 $B %B E& Mweka/classifiers/trees/j48Consolidated/C45ConsolidatedPruneableClassifierTree '( weka/core/Instances E] )F gh \* ,weka/classifiers/trees/j48/C45ModelSelection +F ,] <weka/classifiers/trees/j48Consolidated/InstancesConsolidated -B. /0 (=== Generation of the set of samples ===1 23 ²M 4Ï 50 8Original data size is 0! Handle zero training instances! Original data size: 6 Size of samples is 0 ( % of ")! Handle zero training instances! &Size of samples, m_RMbagSizePercent, ( T) has to be between 0 and 100, when m_RMnewDistrMinClass < 0 (stratified or free)!!! E3 java/util/Random 72 E8 9: rs s s /=== End of Generation of the set of samples === Ï ;< =>? @A BC 52 DE New bag size: Classes sizes of the new bag: FÑ , F3 java/lang/Double @G Ratio bag:sample by each class: /(*) The most disfavored class based on coverage HI (*) JK LK - (*) Forced the number of samples to be 3!!!
34 java/lang/Math MN OP QR ES T ÆIn the case of multi-class datasets, the only posibility to change the distribution of classes is to balance them!!!
Use the special value '50.0' in <distribution minority class> for this purpose!!! UV WA 60 ;Minimum number of examples to be guaranteed in each class: (*) Forced the -th class to be oversampled!!!
XY Z[ \ ]^ ; (*) Forced to reduce the size of the generated samples!!!
%There aren't enough instances of the -th class (
) to extract , for the new samples whithout replacement!!! java/util/Vector _È `a weka/core/Option bc b Set the number of samples to be generated based on a coverage value
as a percentage (by default) RM-C -RM-C Ed[ Number of samples to be generated for the use in the construction of the
consolidated tree.
It can be set as a fixed value or based on a coverage value as a percentage,
when -RM-C option is used, which guarantees the number of samples necessary
to adequately cover the examples of the original sample.
(default: 5 for a fixed value and
)% for the case based on a coverage value) RM-N -RM-N <Number of samples> @ Use replacement to generate the set of samples
(default false) RM-R -RM-RL Size of each sample(bag), as a percentage of the training set size.
Combined with the option <distribution minority class> accepts:
* -1 (sizeOfMinClass): The size of the minority class
* -2 (maxSize): Maximum size taking <distribution minority class>
into account and using no replacement
(default -2(maxSize)) RM-B -RM-B <Size of each sample(%)>& Determines the new value of the distribution of the minority class.
It can be one of the following values:
* A value between 0 and 100 to change the portion of minority class
instances in the new samples
(If the dataset is multi-class, only the special value 50.0 will
be accepted to balance the classes)
* -1 (free): Works with the instances without taking their class
into account
* -2 (stratified): Maintains the original class distribution in the
new samples
(default 50.0) RM-D #-RM-D <distribution minority class> e fg weka/core/SelectedTag => Eh »¼ ij kÏ java/lang/Float l ÃÄm no ÐÒ ÕÖ ÉË ¥¦ ® p -Q -RM-N q -RM-B -RM-D rÏ java/lang/String st No classifier built J48Consolidated unpruned tree
u J48Consolidated pruned tree
[RM] N_S= f( % of coverage) = stratified free distribution %Min= balanced Size= maxSize sizeOfMinClass % (with replacement) (without replacement)
True coverage achieved: v wx ÂWay to set the number of samples to be generated:
* using a fixed value which directly indicates the number of samples to be generated
* based on a coverage value as a percentage (by default)
yz {| }Ï "java/lang/IllegalArgumentException 6Wrong selection type, value should be: between 1 and 2 Number of samples to be generated for the use in the consolidation process (fixed value) or based on a coverage value as a %.
* if RMnumberSamplesHowToSet == ~M T A positive value which directly indicates the number of samples to be generated
! * if RMnumberSamplesHowToSet == k A positive value as a percentage, the coverage value, which guarantees the number of samples necessary
< to adequately cover the examples of the original sample
" (default: 5 for a fixed value or .Number of samples has to be greater than zero! Number of samples is 0. It doesn't make sense to build a consolidated tree without set of samples. Handle zero training instances! +Coverage value has to be greater than zero! Coverage value is 0. It doesn't make sense to build a consolidated tree without set of samples. Handle zero training instances! @Whether replacement is performed to generate the set of samples. ×Ø{Size of each sample(bag), as a percentage of the training set size/-1=sizeOfMinClass/-2=maxSize.
Combined with the option <distribution minority class>, RMnewDistrMinClass, accepts:
* -1 (sizeOfMinClass): The size of the minority class
* -2 (maxSize): Maximum size taking <distribution minority class> into account
and using no replacement. (default: -2 (maxSize)) ¹Size of sample (%) has to be greater than zero and smaller than or equal to 100 (or combining with the option <distribution minority class> -1 for 'sizeOfMinClass' or -2 for 'maxSize')! PSize of sample (%) has to be greater than zero and smaller than or equal to 100!Determines the new value of the distribution of the minority class, if we want to change it/-1=free/-2=stratified.
It can be one of the following values:
* A value between 0 and 100 to change the portion of minority class instances in the new samples
(If the dataset is multi-class, only the special value 50.0 will be accepted to balance the classes)
* -1 (free): Works with the instances without taking their class into account.
* -2 (stratified): Maintains the original class distribution in the new samples.
(default: 50.0) ~Minority class distribution has to be greater than zero and smaller than 100 (or -1 for 'sizeOfMinClass' or -2 for 'maxSize')! RUsing replacement isn't contemplated to change the distribution of minority class! PIt doesn't make sense that size of sample (%) is 100, when replacement is false! .J48 option not implemented for J48Consolidated B java/lang/RuntimeException 2 B ;Seed for random data shuffling in the generation of samples %Number of samples based on coverage: äå âM True coverage: æå ç o êë
weka/core/Tag using a fixed value E
based on a coverage value (%) weka/classifiers/trees/J48 weka/core/OptionHandler weka/core/Drawable weka/core/Matchable weka/classifiers/Sourcable "weka/core/WeightedInstancesHandler weka/core/Summarizable #weka/core/AdditionalMeasureProducer %weka/core/TechnicalInformationHandler )weka/classifiers/trees/j48/ModelSelection java/util/Enumeration append -(Ljava/lang/String;)Ljava/lang/StringBuilder; #weka/core/TechnicalInformation$Type Type InnerClasses ARTICLE %Lweka/core/TechnicalInformation$Type; ((Lweka/core/TechnicalInformation$Type;)V $weka/core/TechnicalInformation$Field Field AUTHOR &Lweka/core/TechnicalInformation$Field; setValue ;(Lweka/core/TechnicalInformation$Field;Ljava/lang/String;)V YEAR TITLE JOURNAL VOLUME NUMBER PAGES URL add #(Lweka/core/TechnicalInformation;)V
m_unpruned m_CF m_subtreeRaising m_noCleanup m_collapseTree 3(Lweka/classifiers/trees/j48/ModelSelection;ZFZZZ)V "(Lweka/core/CapabilitiesHandler;)V setOwner m_minNumObj m_useMDLcorrection m_doNotMakeSplitPointActualValue (ILweka/core/Instances;ZZ)V m_root +Lweka/classifiers/trees/j48/ClassifierTree; deleteWithMissingClass .(Lweka/core/Instances;[Lweka/core/Instances;)V cleanup testWithFail m_Debug java/lang/System out Ljava/io/PrintStream; java/io/PrintStream println (Ljava/lang/String;)V numInstances err (I)Ljava/lang/StringBuilder; m_Seed (J)V getRandomNumberGenerator (J)Ljava/util/Random;
getClasses A()[Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated; getClassesSize C([Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated;)[I weka/core/Utils minIndex ([I)I printClassesInformation (II[I)V round (D)I print ([D)I doubleToString (DI)Ljava/lang/String; log (D)D ceil pow (DD)D resampleWithWeights )(Ljava/util/Random;)Lweka/core/Instances; randomize (Ljava/util/Random;)V (Lweka/core/Instances;II)V A(Lweka/classifiers/trees/j48Consolidated/InstancesConsolidated;)V sort ([I)[I maxIndex nextInt (I)I instance (I)Lweka/core/Instance; (Lweka/core/Instance;)Z eq (DD)Z hasMoreElements nextElement ()Ljava/lang/Object;
addElement (Ljava/lang/Object;)V :(Ljava/lang/String;Ljava/lang/String;ILjava/lang/String;)V elements getFlag ((Ljava/lang/String;[Ljava/lang/String;)Z (I[Lweka/core/Tag;)V getOption 9(Ljava/lang/String;[Ljava/lang/String;)Ljava/lang/String; length
floatValue java/lang/Integer parseInt (Ljava/lang/String;)I (Ljava/lang/Object;)Z (F)Ljava/lang/StringBuilder; size toArray (([Ljava/lang/Object;)[Ljava/lang/Object; )weka/classifiers/trees/j48/ClassifierTree (D)Ljava/lang/StringBuilder; valueOf ([C)Ljava/lang/String; getTags ()[Lweka/core/Tag; getSelectedTag ()Lweka/core/Tag; getID getReadable m_reducedErrorPruning
m_numFolds m_binarySplits compareToIgnoreCase
runClassifier 3(Lweka/classifiers/Classifier;[Ljava/lang/String;)V (ILjava/lang/String;)V ! ! "#$%&'() *+ , -
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localModel 1Lweka/classifiers/trees/j48/ClassifierSplitModel; iSample iAtt data
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SourceFile "C45ConsolidatedModelSelection.java + , ,weka/classifiers/trees/j48/C45ModelSelection ) * 9 #weka/classifiers/trees/j48/C45Split ?weka/classifiers/trees/j48Consolidated/DistributionConsolidated + "weka/classifiers/trees/j48/NoSplit + ^ _ ;weka/classifiers/trees/j48Consolidated/C45ConsolidatedSplit 3 7 + ¡ ¢ java/lang/Double £ ¤ ¥ ¦ ¢ weka/core/matrix/DoubleVector + § ¨ © ª « § ¬ Dweka/classifiers/trees/j48Consolidated/C45ConsolidatedModelSelection weka/core/Instances /weka/classifiers/trees/j48/ClassifierSplitModel 'weka/classifiers/trees/j48/Distribution java/lang/Exception )weka/classifiers/trees/j48/ModelSelection H(Lweka/core/Instances;)Lweka/classifiers/trees/j48/ClassifierSplitModel;
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samplesVector [Lweka/core/Instances;
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numberSamples r
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numberSamples distributionVector B[Lweka/classifiers/trees/j48Consolidated/DistributionConsolidated;
StackMapTable K ' *
Exceptions ^ J([Lweka/core/Instances;Lweka/classifiers/trees/j48/ClassifierSplitModel;)V sampleWithoutMissing Lweka/core/Instances;
modelToUse 1Lweka/classifiers/trees/j48/ClassifierSplitModel; attIndex _ (Lweka/core/Instances;)V instances I(Lweka/core/Instances;Lweka/classifiers/trees/j48/ClassifierSplitModel;)V calculateMeanDistribution E([Lweka/classifiers/trees/j48Consolidated/DistributionConsolidated;)V iClass iBag
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SourceFile DistributionConsolidated.java ` a b c ?weka/classifiers/trees/j48Consolidated/DistributionConsolidated 8 ; < _ d b #weka/classifiers/trees/j48/C45Split 6 b e f : g h @ A i b j k l m n k o p C D @ q 'weka/classifiers/trees/j48/Distribution java/lang/Exception /weka/classifiers/trees/j48/ClassifierSplitModel weka/core/Instances
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