Serialized Form


Package weka.classifiers.rules

Class weka.classifiers.rules.ConjunctiveRule extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -5938309903225087198L

Serialized Fields

m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for REP


m_ClassAttribute

weka.core.Attribute m_ClassAttribute
The class attribute of the data


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule


m_DefDstr

double[] m_DefDstr
The default rule distribution of the data not covered


m_Cnsqt

double[] m_Cnsqt
The consequent of this rule


m_NumClasses

int m_NumClasses
Number of classes in the training data


m_Seed

long m_Seed
The seed to perform randomization


m_Random

java.util.Random m_Random
The Random object used for randomization


m_Targets

weka.core.FastVector m_Targets
The predicted classes recorded for each antecedent in the growing data


m_IsExclude

boolean m_IsExclude
Whether to use exlusive expressions for nominal attributes


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_NumAntds

int m_NumAntds
The number of antecedents in pre-pruning

Class weka.classifiers.rules.CopyOfFR3 extends weka.classifiers.RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 3493596602554738869L

Serialized Fields

m_Classifiers

weka.classifiers.Classifier[] m_Classifiers
The classifiers.


m_ClassFilters

weka.filters.Filter[] m_ClassFilters
The filters used to transform the class.


m_ClassAttribute

weka.core.Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

weka.core.Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method


m_learnBothModelsForEachPairOfClasses

boolean m_learnBothModelsForEachPairOfClasses
Switch which determines whether all n(n-1) models are learned or only n(n-1)/2 models.


aprioriClassDistribution

double[] aprioriClassDistribution
An array which stores the class distribution of the training data


m_safeModelToDiskAfterLearning

boolean m_safeModelToDiskAfterLearning
A switch which determines whether the binary models should be cached to the harddrive (to free RAM for other classifiers temporarily)


m_modelSafePath

java.lang.String m_modelSafePath
The path to which the binary models are written


m_filesWereLoaded

boolean m_filesWereLoaded
A flag which shows that the saved models may have already been loaded to the RAM


m_filenamePattern

java.lang.String m_filenamePattern
The filename which is used for pairwise models which are written to disk.


m_Random

java.util.Random m_Random
The random variable generator

Class weka.classifiers.rules.CopyOfFR3TBM extends weka.classifiers.rules.FR3 implements Serializable

serialVersionUID: 314383984608529442L

Class weka.classifiers.rules.CopyOfFRIP extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -1633447825564158284L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


dataspaceEdges

double[][] dataspaceEdges
The edges of the known dataspace


m_dataAllClasses

weka.core.Instances m_dataAllClasses

Class weka.classifiers.rules.CopyOfFRIP.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data

Class weka.classifiers.rules.CopyOfFRIP.NominalAntd extends weka.classifiers.rules.CopyOfFRIP.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.CopyOfFRIP.NumericAntd extends weka.classifiers.rules.CopyOfFRIP.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.CopyOfFRIP.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule


m_confidence

double m_confidence

Class weka.classifiers.rules.CopyOfFURIA extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


aprioriDistribution

double[] aprioriDistribution

m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


m_useRuleStretching

boolean m_useRuleStretching
Whether use rule stretching


kNNClassifier

weka.classifiers.lazy.IB1 kNNClassifier

m_orderedData

weka.core.Instances m_orderedData

antecedentSet

java.util.HashSet<E>[] antecedentSet

Class weka.classifiers.rules.CopyOfFURIA.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data


weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

double weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

m_confidence

double m_confidence

Class weka.classifiers.rules.CopyOfFURIA.NominalAntd extends weka.classifiers.rules.CopyOfFURIA.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.CopyOfFURIA.NumericAntd extends weka.classifiers.rules.CopyOfFURIA.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.CopyOfFURIA.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_confidence

double m_confidence

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.DecisionTable extends weka.classifiers.Classifier implements Serializable

serialVersionUID: 2888557078165701326L

Serialized Fields

m_entries

java.util.Hashtable<K,V> m_entries
The hashtable used to hold training instances


m_classPriorCounts

double[] m_classPriorCounts
The class priors to use when there is no match in the table


m_classPriors

double[] m_classPriors

m_decisionFeatures

int[] m_decisionFeatures
Holds the final feature set


m_disTransform

weka.filters.Filter m_disTransform
Discretization filter


m_delTransform

weka.filters.unsupervised.attribute.Remove m_delTransform
Filter used to remove columns discarded by feature selection


m_ibk

weka.classifiers.lazy.IBk m_ibk
IB1 used to classify non matching instances rather than majority class


m_theInstances

weka.core.Instances m_theInstances
Holds the original training instances


m_dtInstances

weka.core.Instances m_dtInstances
Holds the final feature selected set of instances


m_numAttributes

int m_numAttributes
The number of attributes in the dataset


m_numInstances

int m_numInstances
The number of instances in the dataset


m_classIsNominal

boolean m_classIsNominal
Class is nominal


m_useIBk

boolean m_useIBk
Use the IBk classifier rather than majority class


m_displayRules

boolean m_displayRules
Display Rules


m_CVFolds

int m_CVFolds
Number of folds for cross validating feature sets


m_rr

java.util.Random m_rr
Random numbers for use in cross validation


m_majority

double m_majority
Holds the majority class


m_search

weka.attributeSelection.ASSearch m_search
The search method to use


m_evaluator

weka.attributeSelection.ASEvaluation m_evaluator
Our own internal evaluator


m_evaluation

weka.classifiers.Evaluation m_evaluation
The evaluation object used to evaluate subsets


m_evaluationMeasure

int m_evaluationMeasure

m_saveMemory

boolean m_saveMemory

Class weka.classifiers.rules.DecisionTableHashKey extends java.lang.Object implements Serializable

serialVersionUID: 5674163500154964602L

Serialized Fields

attributes

double[] attributes
Array of attribute values for an instance


missing

boolean[] missing
True for an index if the corresponding attribute value is missing.


key

int key
The key

Class weka.classifiers.rules.DTNB extends weka.classifiers.rules.DecisionTable implements Serializable

serialVersionUID: 2999557077765701326L

Serialized Fields

m_NB

weka.classifiers.bayes.NaiveBayes m_NB
The naive Bayes half of the hybrid


m_nbFeatures

int[] m_nbFeatures
The features used by naive Bayes


m_percentUsedByDT

double m_percentUsedByDT
Percentage of the total number of features used by the decision table


m_percentDeleted

double m_percentDeleted
Percentage of the features features that were dropped entirely


m_backwardWithDelete

weka.attributeSelection.ASSearch m_backwardWithDelete

Class weka.classifiers.rules.DTNB.BackwardsWithDelete extends weka.attributeSelection.ASSearch implements Serializable

Class weka.classifiers.rules.DTNB.EvalWithDelete extends weka.attributeSelection.ASEvaluation implements Serializable

Serialized Fields

m_deletedFromDTNB

java.util.BitSet m_deletedFromDTNB

Class weka.classifiers.rules.FR3 extends weka.classifiers.RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 3493596602554738869L

Serialized Fields

m_Classifiers

weka.classifiers.Classifier[] m_Classifiers
The classifiers.


m_ClassFilters

weka.filters.Filter[] m_ClassFilters
The filters used to transform the class.


m_ClassAttribute

weka.core.Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

weka.core.Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method


m_learnBothModelsForEachPairOfClasses

boolean m_learnBothModelsForEachPairOfClasses
Switch which determines whether all n(n-1) models are learned or only n(n-1)/2 models.


aprioriClassDistribution

double[] aprioriClassDistribution
An array which stores the class distribution of the training data


m_safeModelToDiskAfterLearning

boolean m_safeModelToDiskAfterLearning
A switch which determines whether the binary models should be cached to the harddrive (to free RAM for other classifiers temporarily)


m_modelSafePath

java.lang.String m_modelSafePath
The path to which the binary models are written


m_filesWereLoaded

boolean m_filesWereLoaded
A flag which shows that the saved models may have already been loaded to the RAM


m_filenamePattern

java.lang.String m_filenamePattern
The filename which is used for pairwise models which are written to disk.


m_Random

java.util.Random m_Random
The random variable generator

Class weka.classifiers.rules.FR3_REJECTION_TEST extends weka.classifiers.RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 3493596602554738869L

Serialized Fields

m_Classifiers

weka.classifiers.Classifier[] m_Classifiers
The classifiers.


m_ClassFilters

weka.filters.Filter[] m_ClassFilters
The filters used to transform the class.


m_ClassAttribute

weka.core.Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

weka.core.Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method


m_learnBothModelsForEachPairOfClasses

boolean m_learnBothModelsForEachPairOfClasses
Switch which determines whether all n(n-1) models are learned or only n(n-1)/2 models.


aprioriClassDistribution

double[] aprioriClassDistribution
An array which stores the class distribution of the training data


m_safeModelToDiskAfterLearning

boolean m_safeModelToDiskAfterLearning
A switch which determines whether the binary models should be cached to the harddrive (to free RAM for other classifiers temporarily)


m_modelSafePath

java.lang.String m_modelSafePath
The path to which the binary models are written


m_filesWereLoaded

boolean m_filesWereLoaded
A flag which shows that the saved models may have already been loaded to the RAM


m_filenamePattern

java.lang.String m_filenamePattern
The filename which is used for pairwise models which are written to disk.


m_Random

java.util.Random m_Random
The random variable generator

Class weka.classifiers.rules.FR3BB extends weka.classifiers.RandomizableSingleClassifierEnhancer implements Serializable

serialVersionUID: 3493596602554738869L

Serialized Fields

m_Classifiers

weka.classifiers.Classifier[] m_Classifiers
The classifiers.


m_ClassFilters

weka.filters.Filter[] m_ClassFilters
The filters used to transform the class.


m_ClassAttribute

weka.core.Attribute m_ClassAttribute
Internal copy of the class attribute for output purposes


m_TwoClassDataset

weka.core.Instances m_TwoClassDataset
A transformed dataset header used by the 1-against-1 method


m_learnBothModelsForEachPairOfClasses

boolean m_learnBothModelsForEachPairOfClasses
Switch which determines whether all n(n-1) models are learned or only n(n-1)/2 models.


aprioriClassDistribution

double[] aprioriClassDistribution
An array which stores the class distribution of the training data


m_safeModelToDiskAfterLearning

boolean m_safeModelToDiskAfterLearning
A switch which determines whether the binary models should be cached to the harddrive (to free RAM for other classifiers temporarily)


m_modelSafePath

java.lang.String m_modelSafePath
The path to which the binary models are written


m_filesWereLoaded

boolean m_filesWereLoaded
A flag which shows that the saved models may have already been loaded to the RAM


m_filenamePattern

java.lang.String m_filenamePattern
The filename which is used for pairwise models which are written to disk.


m_Random

java.util.Random m_Random
The random variable generator

Class weka.classifiers.rules.FR3TBM extends weka.classifiers.rules.FR3BB implements Serializable

serialVersionUID: 314383984608529442L

Class weka.classifiers.rules.FRIP extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -1633447825564158284L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


dataspaceEdges

double[][] dataspaceEdges
The edges of the known dataspace


m_dataAllClasses

weka.core.Instances m_dataAllClasses

Class weka.classifiers.rules.FRIP.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data

Class weka.classifiers.rules.FRIP.NominalAntd extends weka.classifiers.rules.FRIP.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.FRIP.NumericAntd extends weka.classifiers.rules.FRIP.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.FRIP.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule


m_confidence

double m_confidence

Class weka.classifiers.rules.FRIPBB extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -1633447825564158284L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


dataspaceEdges

double[][] dataspaceEdges
The edges of the known dataspace


m_dataAllClasses

weka.core.Instances m_dataAllClasses

m_classesAtDataSpaceEdges

double[][][] m_classesAtDataSpaceEdges

Class weka.classifiers.rules.FRIPBB.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data

Class weka.classifiers.rules.FRIPBB.NominalAntd extends weka.classifiers.rules.FRIPBB.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.FRIPBB.NumericAntd extends weka.classifiers.rules.FRIPBB.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.FRIPBB.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule


m_confidence

double m_confidence

Class weka.classifiers.rules.FURA_RD extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


aprioriDistribution

double[] aprioriDistribution

m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


m_useRuleStretching

boolean m_useRuleStretching
Whether use rule stretching


kNNClassifier

weka.classifiers.lazy.IB1 kNNClassifier

m_orderedData

weka.core.Instances m_orderedData

antecedentSet

java.util.HashSet<E>[] antecedentSet

Class weka.classifiers.rules.FURA_RD.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data


weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

double weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

m_confidence

double m_confidence

Class weka.classifiers.rules.FURA_RD.NominalAntd extends weka.classifiers.rules.FURA_RD.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.FURA_RD.NumericAntd extends weka.classifiers.rules.FURA_RD.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.FURA_RD.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_confidence

double m_confidence

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.FURIA extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


aprioriDistribution

double[] aprioriDistribution
The class distribution of the training data


m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


m_useRuleStretching

boolean m_useRuleStretching
Whether use rule stretching

Class weka.classifiers.rules.FURIA.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data


weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

double weightOfTheRuleWhenItIsPrunedAfterThisAntecedent
Confidence / weight of this rule for the rule stretching procedure that is returned when this is the last antecedent of the rule.


m_confidence

double m_confidence
Confidence / weight of this antecedent.

Class weka.classifiers.rules.FURIA.NominalAntd extends weka.classifiers.rules.FURIA.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.FURIA.NumericAntd extends weka.classifiers.rules.FURIA.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.FURIA.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.GridFURIA extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


aprioriDistribution

double[] aprioriDistribution

m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value


m_useRuleStretching

boolean m_useRuleStretching
Whether use rule stretching


kNNClassifier

weka.classifiers.lazy.IB1 kNNClassifier

antecedentSet

java.util.HashSet<E>[] antecedentSet

m_FuzzyRuleSet

weka.core.FastVector m_FuzzyRuleSet

fp

weka.fuzzylogic.FuzzyPartition[] fp

Class weka.classifiers.rules.GridFURIA.Antd extends java.lang.Object implements Serializable

Serialized Fields

att

weka.core.Attribute att
The attribute of the antecedent


value

double value
The attribute value of the antecedent. For numeric attribute, value is either 0(1st bag) or 1(2nd bag)


maxInfoGain

double maxInfoGain
The maximum infoGain achieved by this antecedent test in the growing data


accuRate

double accuRate
The accurate rate of this antecedent test on the growing data


cover

double cover
The coverage of this antecedent in the growing data


accu

double accu
The accurate data for this antecedent in the growing data


weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

double weightOfTheRuleWhenItIsPrunedAfterThisAntecedent

m_confidence

double m_confidence

Class weka.classifiers.rules.GridFURIA.NominalAntd extends weka.classifiers.rules.GridFURIA.Antd implements Serializable

serialVersionUID: -9102297038837585135L

Serialized Fields

accurate

double[] accurate

coverage

double[] coverage

Class weka.classifiers.rules.GridFURIA.NumericAntd extends weka.classifiers.rules.GridFURIA.Antd implements Serializable

serialVersionUID: 5699457269983735442L

Serialized Fields

splitPoint

double splitPoint
The split point for this numeric antecedent


supportBound

double supportBound
The edge point for the fuzzy set of this numeric antecedent

Class weka.classifiers.rules.GridFURIA.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_confidence

double m_confidence

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.JRip extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -6589312996832147161L

Serialized Fields

m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Ruleset

weka.core.FastVector m_Ruleset
The ruleset


m_Distributions

weka.core.FastVector m_Distributions
The predicted class distribution


m_Optimizations

int m_Optimizations
Runs of optimizations


m_Random

java.util.Random m_Random
Random object used in this class


m_Total

double m_Total
# of all the possible conditions in a rule


m_Seed

long m_Seed
The seed to perform randomization


m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_MinNo

double m_MinNo
The minimal number of instance weights within a split


m_Debug

boolean m_Debug
Whether in a debug mode


m_CheckErr

boolean m_CheckErr
Whether check the error rate >= 0.5 in stopping criteria


m_UsePruning

boolean m_UsePruning
Whether use pruning, i.e. the data is clean or not


m_Filter

weka.filters.Filter m_Filter
The filter used to randomize the class order


m_RulesetStats

weka.core.FastVector m_RulesetStats
The RuleStats for the ruleset of each class value

Class weka.classifiers.rules.JRip.RipperRule extends weka.classifiers.rules.Rule implements Serializable

serialVersionUID: -2410020717305262952L

Serialized Fields

m_Consequent

double m_Consequent
The internal representation of the class label to be predicted


m_Antds

weka.core.FastVector m_Antds
The vector of antecedents of this rule

Class weka.classifiers.rules.M5Rules extends weka.classifiers.trees.m5.M5Base implements Serializable

serialVersionUID: -1746114858746563180L

Class weka.classifiers.rules.NNge extends weka.classifiers.Classifier implements Serializable

serialVersionUID: 4084742275553788972L

Serialized Fields

m_Train

weka.core.Instances m_Train
An empty instances to keep the headers, the classIndex, etc...


m_Exemplars

weka.classifiers.rules.NNge.Exemplar m_Exemplars
The list of Exemplars


m_ExemplarsByClass

weka.classifiers.rules.NNge.Exemplar[] m_ExemplarsByClass
The lists of Exemplars by class


m_MinArray

double[] m_MinArray
The minimum values for numeric attributes.


m_MaxArray

double[] m_MaxArray
The maximum values for numeric attributes.


m_NumAttemptsOfGene

int m_NumAttemptsOfGene
The number of try for generalisation


m_NumFoldersMI

int m_NumFoldersMI
The number of folder for the Mutual Information


m_MissingVector

double[] m_MissingVector
Values to use for missing value


m_MI_NumAttrClassInter

int[][][] m_MI_NumAttrClassInter
MUTUAL INFORMATION'S DATAS


m_MI_NumAttrInter

int[][] m_MI_NumAttrInter

m_MI_MaxArray

double[] m_MI_MaxArray

m_MI_MinArray

double[] m_MI_MinArray

m_MI_NumAttrClassValue

int[][][] m_MI_NumAttrClassValue

m_MI_NumAttrValue

int[][] m_MI_NumAttrValue

m_MI_NumClass

int[] m_MI_NumClass

m_MI_NumInst

int m_MI_NumInst

m_MI

double[] m_MI

Class weka.classifiers.rules.OneR extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -2459427002147861445L

Serialized Fields

m_rule

weka.classifiers.rules.OneR.OneRRule m_rule
A 1-R rule


m_minBucketSize

int m_minBucketSize
The minimum bucket size


m_ZeroR

weka.classifiers.Classifier m_ZeroR
a ZeroR model in case no model can be built from the data

Class weka.classifiers.rules.PART extends weka.classifiers.Classifier implements Serializable

serialVersionUID: 8121455039782598361L

Serialized Fields

m_root

weka.classifiers.rules.part.MakeDecList m_root
The decision list


m_CF

float m_CF
Confidence level


m_minNumObj

int m_minNumObj
Minimum number of objects


m_reducedErrorPruning

boolean m_reducedErrorPruning
Use reduced error pruning?


m_numFolds

int m_numFolds
Number of folds for reduced error pruning.


m_binarySplits

boolean m_binarySplits
Binary splits on nominal attributes?


m_unpruned

boolean m_unpruned
Generate unpruned list?


m_Seed

int m_Seed
The seed for random number generation.

Class weka.classifiers.rules.Prism extends weka.classifiers.Classifier implements Serializable

serialVersionUID: 1310258880025902106L

Serialized Fields

m_rules

weka.classifiers.rules.Prism.PrismRule m_rules
The first rule in the list of rules

Class weka.classifiers.rules.Ridor extends weka.classifiers.Classifier implements Serializable

serialVersionUID: -7261533075088314436L

Serialized Fields

m_Folds

int m_Folds
The number of folds to split data into Grow and Prune for IREP


m_Shuffle

int m_Shuffle
The number of shuffles performed on the data for randomization


m_Random

java.util.Random m_Random
Random object for randomization


m_Seed

int m_Seed
The seed to perform randomization


m_IsAllErr

boolean m_IsAllErr
Whether use error rate on all the data


m_IsMajority

boolean m_IsMajority
Whether use majority class as default class


m_Root

weka.classifiers.rules.Ridor.Ridor_node m_Root
The root of Ridor


m_Class

weka.core.Attribute m_Class
The class attribute of the data


m_Cover

double m_Cover
Statistics of the data


m_Err

double m_Err
Statistics of the data


m_MinNo

double m_MinNo
The minimal number of instance weights within a split

Class weka.classifiers.rules.Rule extends java.lang.Object implements Serializable

serialVersionUID: 8815687740470471229L

Class weka.classifiers.rules.RuleStats extends java.lang.Object implements Serializable

serialVersionUID: -5708153367675298624L

Serialized Fields

m_Data

weka.core.Instances m_Data
The data on which the stats calculation is based


m_Ruleset

weka.core.FastVector m_Ruleset
The specific ruleset in question


m_SimpleStats

weka.core.FastVector m_SimpleStats
The simple stats of each rule


m_Filtered

weka.core.FastVector m_Filtered
The set of instances filtered by the ruleset


m_Total

double m_Total
The total number of possible conditions that could appear in a rule


MDL_THEORY_WEIGHT

double MDL_THEORY_WEIGHT
The theory weight in the MDL calculation


m_Distributions

weka.core.FastVector m_Distributions
The class distributions predicted by each rule

Class weka.classifiers.rules.ZeroR extends weka.classifiers.Classifier implements Serializable

serialVersionUID: 48055541465867954L

Serialized Fields

m_ClassValue

double m_ClassValue
The class value 0R predicts.


m_Counts

double[] m_Counts
The number of instances in each class (null if class numeric).


m_Class

weka.core.Attribute m_Class
The class attribute.