- getAlgorithmType() - Method in class weka.classifiers.mi.MILR
-
Gets the type of algorithm.
- getAttributesToSplit() - Method in class weka.classifiers.mi.MITI
-
Getter method.
- getB() - Method in class weka.classifiers.mi.MITI
-
Getter for B.
- getBa() - Method in class weka.classifiers.mi.MITI
-
Getter for Ba.
- GetBEPP(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.BEPP
-
Calculates score for entire set based on given sufficient statistics and parameters.
- GetBEPP(double, double, int, boolean) - Static method in class weka.classifiers.mi.miti.BEPP
-
Calculates score based on given counts and parameters.
- getBepp(List<Instance>, HashMap<Instance, Bag>, boolean, int, boolean, double) - Static method in class weka.classifiers.mi.miti.NextSplitHeuristic
-
Method used to get the BEPP scores based on the given arguments.
- getBestSplitPoint(Attribute, ArrayList<Instance>, HashMap<Instance, Bag>, AlgorithmConfiguration) - Static method in class weka.classifiers.mi.miti.Split
-
Finds the best split based on the given arguments.
- getBuildLogisticModels() - Method in class weka.classifiers.mi.MISMO
-
Get the value of buildLogisticModels.
- getC() - Method in class weka.classifiers.mi.MISMO
-
Get the value of C.
- getC() - Method in class weka.classifiers.mi.MISVM
-
Get the value of C.
- getCapabilities() - Method in class weka.classifiers.mi.MDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIBoost
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIEMDD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MILR
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MINND
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MISMO
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MISVM
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MITI
-
Returns the capabilities of this classifier.
- getCapabilities() - Method in class weka.classifiers.mi.MIWrapper
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.SimpleMI
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the Capabilities of this kernel.
- getCapabilities() - Method in class weka.classifiers.mi.TLC
-
Returns the Capabilities of this filter.
- getCapabilities() - Method in class weka.classifiers.mi.TLD
-
Returns default capabilities of the classifier.
- getCapabilities() - Method in class weka.classifiers.mi.TLDSimple
-
Returns default capabilities of the classifier.
- getChecksTurnedOff() - Method in class weka.classifiers.mi.MISMO
-
Returns whether the checks are turned off or not.
- getConsiderBothClasses() - Method in class weka.classifiers.mi.QuickDDIterative
-
Get wether to consider both classes as "positive" class in turn.
- getDiscretizeBin() - Method in class weka.classifiers.mi.MIBoost
-
Get the number of bins in discretization
- getEpsilon() - Method in class weka.classifiers.mi.MISMO
-
Get the value of epsilon.
- getFilterType() - Method in class weka.classifiers.mi.MDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIEMDD
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MIOptimalBall
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MISMO
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.MISVM
-
Gets how the training data will be transformed.
- getFilterType() - Method in class weka.classifiers.mi.QuickDDIterative
-
Gets how the training data will be transformed.
- getGiniImpurity(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.Gini
-
Returns Gini impurity score of the two groups after the split - the closer to zero, the better the split.
- getGiniImpurity(double[], double[], int, boolean) - Static method in class weka.classifiers.mi.miti.Gini
-
Returns Gini impurity score for the N groups after a nominal split (the closer to zero, the better the split).
- getK() - Method in class weka.classifiers.mi.MITI
-
Getter for K.
- getKernel() - Method in class weka.classifiers.mi.MISMO
-
Gets the kernel to use.
- getKernel() - Method in class weka.classifiers.mi.MISVM
-
Gets the kernel to use.
- getL() - Method in class weka.classifiers.mi.MITI
-
Getter for L.
- GetLeftBEPP(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.BEPP
-
Calculates score for left subset based on given sufficient statistics and parameters.
- getMaxBEPP(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.MaxBEPP
-
Computes MaxBEPP score for two subsets; the larger the better.
- getMaxBEPP(double[], double[], int, boolean) - Static method in class weka.classifiers.mi.miti.MaxBEPP
-
Computes MaxBEPP score for nominal case; the larger the better.
- getMaxIterations() - Method in class weka.classifiers.mi.MIBoost
-
Get the maximum number of boost iterations
- getMaxIterations() - Method in class weka.classifiers.mi.MISVM
-
Gets the maximum number of iterations.
- getMaxIterations() - Method in class weka.classifiers.mi.QuickDDIterative
-
- getMaxProbNegativeClass() - Method in class weka.classifiers.mi.QuickDDIterative
-
Get the maximum probability for the negative class.
- getMeasure(String) - Method in class weka.classifiers.mi.MIRI
-
Returns the value of the named measure.
- getMeasure(String) - Method in class weka.classifiers.mi.MITI
-
Returns the value of the named measure.
- getMethod() - Method in class weka.classifiers.mi.MIWrapper
-
Get the method used in testing.
- getMinimax() - Method in class weka.classifiers.mi.MISMO
-
Check if the MIMinimax feature space is to be used.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIBoost
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIEMDD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MILR
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MINND
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISMO
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISVM
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MITI
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.SimpleMI
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the capabilities of this multi-instance kernel for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the capabilities of this multi-instance kernel for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.TLC
-
Returns the capabilities of this multi-instance filter for the relational
data (i.e., the bags).
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.TLD
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.TLDSimple
-
Returns the capabilities of this multi-instance classifier for the
relational data.
- getNumFolds() - Method in class weka.classifiers.mi.MISMO
-
Get the value of numFolds.
- getNumNeighbours() - Method in class weka.classifiers.mi.MINND
-
Returns the number of nearest neighbours to estimate the class prediction
of tests bags
- getNumRuns() - Method in class weka.classifiers.mi.TLD
-
Returns the number of runs to perform.
- getNumRuns() - Method in class weka.classifiers.mi.TLDSimple
-
Returns the number of runs to perform.
- getNumTestingNoises() - Method in class weka.classifiers.mi.MINND
-
Returns The number of nearest neighbour instances in the selection of
noises in the test data
- getNumTrainingNoises() - Method in class weka.classifiers.mi.MINND
-
Returns the number of nearest neighbour instances in the selection of
noises in the training data
- getOptions() - Method in class weka.classifiers.mi.MDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIBoost
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MIEMDD
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MILR
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MINND
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.MIOptimalBall
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MISMO
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MISVM
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.MITI
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.MIWrapper
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.QuickDDIterative
-
Gets the current settings of the classifier.
- getOptions() - Method in class weka.classifiers.mi.SimpleMI
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.TLC
-
Gets the current settings of the filter.
- getOptions() - Method in class weka.classifiers.mi.TLD
-
Gets the current settings of the Classifier.
- getOptions() - Method in class weka.classifiers.mi.TLDSimple
-
Gets the current settings of the Classifier.
- getPartitionGenerator() - Method in class weka.classifiers.mi.TLC
-
Get the generator used by this filter
- getRandomSeed() - Method in class weka.classifiers.mi.MISMO
-
Get the value of randomSeed.
- getRevision() - Method in class weka.classifiers.mi.MDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIBoost
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIEMDD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MILR
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MINND
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MISMO
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MISVM
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns the revision string
- getRevision() - Method in class weka.classifiers.mi.SimpleMI
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.TLC
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.TLD
-
Returns the revision string.
- getRevision() - Method in class weka.classifiers.mi.TLDSimple
-
Returns the revision string.
- getRidge() - Method in class weka.classifiers.mi.MILR
-
Gets the ridge in the log-likelihood.
- GetRightBEPP(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.BEPP
-
Calculates score for right subset based on given sufficient statistics and parameters.
- getScalingFactor() - Method in class weka.classifiers.mi.QuickDDIterative
-
Get the scaling factor for the Gaussian-like function at the target point.
- getScore(SufficientStatistics, int, boolean) - Method in class weka.classifiers.mi.miti.Gini
-
Returns purity score of the two groups after the split - larger is better
- getScore(double[], double[], int, boolean) - Method in class weka.classifiers.mi.miti.Gini
-
Returns purity score for the N groups after a nominal split - larger is better
- getScore(SufficientStatistics, int, boolean) - Method in interface weka.classifiers.mi.miti.IBestSplitMeasure
-
Returns a purity score of the two groups after the split - larger is better
- getScore(double[], double[], int, boolean) - Method in interface weka.classifiers.mi.miti.IBestSplitMeasure
-
Returns a purity score for the N groups after a nominal split - larger is better
- getScore(SufficientStatistics, int, boolean) - Method in class weka.classifiers.mi.miti.MaxBEPP
-
Computes MaxBEPP score for two subsets; the larger the better.
- getScore(double[], double[], int, boolean) - Method in class weka.classifiers.mi.miti.MaxBEPP
-
Computes MaxBEPP score for nominal case; the larger the better.
- getScore(SufficientStatistics, int, boolean) - Method in class weka.classifiers.mi.miti.SSBEPP
-
Returns SSBEPP score of the two groups after the split - the larger the better
- getScore(double[], double[], int, boolean) - Method in class weka.classifiers.mi.miti.SSBEPP
-
Stub: implementation for nominal attributes not complete; will simply exit.
- getSplitMethod() - Method in class weka.classifiers.mi.MITI
-
Getter method.
- getSSBEPP(SufficientStatistics, int, boolean) - Static method in class weka.classifiers.mi.miti.SSBEPP
-
Returns SSBEPP score of the two groups after the split - the larger the better
- getTechnicalInformation() - Method in class weka.classifiers.mi.MDD
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIBoost
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIDD
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIEMDD
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MINND
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIOptimalBall
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MISMO
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MISVM
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MITI
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.MIWrapper
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.QuickDDIterative
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.TLC
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.TLD
-
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.
- getTechnicalInformation() - Method in class weka.classifiers.mi.TLDSimple
-
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.
- getToleranceParameter() - Method in class weka.classifiers.mi.MISMO
-
Get the value of tolerance parameter.
- getTopNAttributesToSplit() - Method in class weka.classifiers.mi.MITI
-
Getter method.
- getTransformMethod() - Method in class weka.classifiers.mi.SimpleMI
-
Get the method used in transformation.
- getUnbiasedEstimate() - Method in class weka.classifiers.mi.MITI
-
Getter for unbiased estimate flag.
- getUsingCutOff() - Method in class weka.classifiers.mi.TLD
-
Returns whether an empirical cutoff is used
- getUsingCutOff() - Method in class weka.classifiers.mi.TLDSimple
-
Returns whether an empirical cutoff is used
- getWeightMethod() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the current weighting method for instances.
- Gini - Class in weka.classifiers.mi.miti
-
Implements the Gini-based split selection measure.
- Gini() - Constructor for class weka.classifiers.mi.miti.Gini
-
- globalInfo() - Method in class weka.classifiers.mi.MDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIBoost
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIEMDD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MILR
-
Returns the tip text for this property
- globalInfo() - Method in class weka.classifiers.mi.MINND
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIOptimalBall
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MIRI
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.mi.MISMO
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.mi.MISVM
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.MITI
-
Returns a string describing classifier
- globalInfo() - Method in class weka.classifiers.mi.MIWrapper
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.SimpleMI
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.TLC
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.TLD
-
Returns a string describing this filter
- globalInfo() - Method in class weka.classifiers.mi.TLDSimple
-
Returns a string describing this filter
- main(String[]) - Static method in class weka.classifiers.mi.MDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIBoost
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIEMDD
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MILR
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MINND
-
Main method for testing.
- main(String[]) - Static method in class weka.classifiers.mi.MIOptimalBall
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MIRI
-
Used to run the algorithm from the command-line.
- main(String[]) - Static method in class weka.classifiers.mi.MISMO
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MISVM
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.MITI
-
Used to run the classifier from the command-line.
- main(String[]) - Static method in class weka.classifiers.mi.MIWrapper
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.QuickDDIterative
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.SimpleMI
-
Main method for testing this class.
- main(String[]) - Static method in class weka.classifiers.mi.TLC
-
Main method for running this class from the command-line.
- main(String[]) - Static method in class weka.classifiers.mi.TLD
-
Main method for testing.
- main(String[]) - Static method in class weka.classifiers.mi.TLDSimple
-
Main method for testing.
- makeLeafNode(boolean) - Method in class weka.classifiers.mi.miti.TreeNode
-
Turns the node into a leaf node.
- MaxBEPP - Class in weka.classifiers.mi.miti
-
Implements the MaxBEPP split selection measure.
- MaxBEPP() - Constructor for class weka.classifiers.mi.miti.MaxBEPP
-
- maxIterationsTipText() - Method in class weka.classifiers.mi.MIBoost
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.classifiers.mi.MISVM
-
Returns the tip text for this property
- maxIterationsTipText() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns the tip text for this property
- maxProbNegativeClassTipText() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns the tip text for this property
- MDD - Class in weka.classifiers.mi
-
Modified Diverse Density algorithm, with collective
assumption.
More information about DD:
Oded Maron (1998).
- MDD() - Constructor for class weka.classifiers.mi.MDD
-
- method - Variable in class weka.classifiers.mi.miti.AlgorithmConfiguration
-
The method used to score a split (1 = Gini, 2 = Max BEPP, 3 = SSBEPP)
- methodTipText() - Method in class weka.classifiers.mi.MIWrapper
-
Returns the tip text for this property
- MIBoost - Class in weka.classifiers.mi
-
MI AdaBoost method, considers the geometric mean of
posterior of instances inside a bag (arithmatic mean of log-posterior) and
the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see:
Yoav Freund, Robert E.
- MIBoost() - Constructor for class weka.classifiers.mi.MIBoost
-
- MIDD - Class in weka.classifiers.mi
-
Re-implement the Diverse Density algorithm, changes
the testing procedure.
Oded Maron (1998).
- MIDD() - Constructor for class weka.classifiers.mi.MIDD
-
- MIEMDD - Class in weka.classifiers.mi
-
EMDD model builds heavily upon Dietterich's Diverse
Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a
single-instance setting using EM.
- MIEMDD() - Constructor for class weka.classifiers.mi.MIEMDD
-
- MILR - Class in weka.classifiers.mi
-
Uses either standard or collective multi-instance
assumption, but within linear regression.
- MILR() - Constructor for class weka.classifiers.mi.MILR
-
- minBagDistance(Instance, Instance) - Method in class weka.classifiers.mi.MIOptimalBall
-
Calculate the distance from one data point to a bag
- minimax(Instances, int) - Static method in class weka.classifiers.mi.SimpleMI
-
Get the minimal and maximal value of a certain attribute in a certain data
- minimaxTipText() - Method in class weka.classifiers.mi.MISMO
-
Returns the tip text for this property
- MINND - Class in weka.classifiers.mi
-
Multiple-Instance Nearest Neighbour with
Distribution learner.
It uses gradient descent to find the weight for each dimension of each
exeamplar from the starting point of 1.0.
- MINND() - Constructor for class weka.classifiers.mi.MINND
-
- MIOptimalBall - Class in weka.classifiers.mi
-
This classifier tries to find a suitable ball in
the multiple-instance space, with a certain data point in the instance space
as a ball center.
- MIOptimalBall() - Constructor for class weka.classifiers.mi.MIOptimalBall
-
- MIPolyKernel - Class in weka.classifiers.mi.supportVector
-
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
- MIPolyKernel() - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
-
default constructor - does nothing.
- MIPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel
-
Creates a new MIPolyKernel instance.
- MIRBFKernel - Class in weka.classifiers.mi.supportVector
-
The RBF kernel.
- MIRBFKernel() - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
-
default constructor - does nothing.
- MIRBFKernel(Instances, int, double) - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel
-
Constructor.
- MIRI - Class in weka.classifiers.mi
-
MIRI (Multi Instance Rule Inducer): multi-instance classifier that utilizes partial MITI trees witha single positive leaf to learn and represent rules.
- MIRI() - Constructor for class weka.classifiers.mi.MIRI
-
- MISMO - Class in weka.classifiers.mi
-
Implements John Platt's sequential minimal
optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms
nominal attributes into binary ones.
- MISMO() - Constructor for class weka.classifiers.mi.MISMO
-
- MISVM - Class in weka.classifiers.mi
-
Implements Stuart Andrews' mi_SVM (Maximum pattern
Margin Formulation of MIL).
- MISVM() - Constructor for class weka.classifiers.mi.MISVM
-
- MITI - Class in weka.classifiers.mi
-
MITI (Multi Instance Tree Inducer): multi-instance
classification based a decision tree learned using Blockeel et al.'s
algorithm.
- MITI() - Constructor for class weka.classifiers.mi.MITI
-
- MIWrapper - Class in weka.classifiers.mi
-
A simple Wrapper method for applying standard
propositional learners to multi-instance data.
For more information see:
E.
- MIWrapper() - Constructor for class weka.classifiers.mi.MIWrapper
-
- scalingFactorTipText() - Method in class weka.classifiers.mi.QuickDDIterative
-
Returns the tip text for this property
- score - Variable in class weka.classifiers.mi.miti.Split
-
- setAlgorithmType(SelectedTag) - Method in class weka.classifiers.mi.MILR
-
Sets the algorithm type.
- setAttributesToSplit(int) - Method in class weka.classifiers.mi.MITI
-
Setter method.
- setB(boolean) - Method in class weka.classifiers.mi.MITI
-
Setter for B.
- setBa(double) - Method in class weka.classifiers.mi.MITI
-
Setter for Ba.
- setBagWeightMultiplier(double) - Method in class weka.classifiers.mi.miti.Bag
-
The multiplier for the bag weight.
- setBuildLogisticModels(boolean) - Method in class weka.classifiers.mi.MISMO
-
Set the value of buildLogisticModels.
- setC(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of C.
- setC(double) - Method in class weka.classifiers.mi.MISVM
-
Set the value of C.
- setChecksTurnedOff(boolean) - Method in class weka.classifiers.mi.MISMO
-
Disables or enables the checks (which could be time-consuming).
- setConsiderBothClasses(boolean) - Method in class weka.classifiers.mi.QuickDDIterative
-
Set wether to consider both classes as "positive" class in turn.
- setDiscretizeBin(int) - Method in class weka.classifiers.mi.MIBoost
-
Set the number of bins in discretization
- setEpsilon(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of epsilon.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIEMDD
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIOptimalBall
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISMO
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISVM
-
Sets how the training data will be transformed.
- setFilterType(SelectedTag) - Method in class weka.classifiers.mi.QuickDDIterative
-
Sets how the training data will be transformed.
- setK(int) - Method in class weka.classifiers.mi.MITI
-
Setter for K.
- setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO
-
Sets the kernel to use.
- setKernel(Kernel) - Method in class weka.classifiers.mi.MISVM
-
Sets the kernel to use.
- setL(boolean) - Method in class weka.classifiers.mi.MITI
-
Setter for L.
- setMaxIterations(int) - Method in class weka.classifiers.mi.MIBoost
-
Set the maximum number of boost iterations
- setMaxIterations(int) - Method in class weka.classifiers.mi.MISVM
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class weka.classifiers.mi.QuickDDIterative
-
- setMaxProbNegativeClass(double) - Method in class weka.classifiers.mi.QuickDDIterative
-
Set the maximum probability for the negative class.
- setMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
-
Set the method used in testing.
- setMinimax(boolean) - Method in class weka.classifiers.mi.MISMO
-
Set if the MIMinimax feature space is to be used.
- setNumFolds(int) - Method in class weka.classifiers.mi.MISMO
-
Set the value of numFolds.
- setNumNeighbours(int) - Method in class weka.classifiers.mi.MINND
-
Sets the number of nearest neighbours to estimate the class prediction of
tests bags
- setNumRuns(int) - Method in class weka.classifiers.mi.TLD
-
Sets the number of runs to perform.
- setNumRuns(int) - Method in class weka.classifiers.mi.TLDSimple
-
Sets the number of runs to perform.
- setNumTestingNoises(int) - Method in class weka.classifiers.mi.MINND
-
Sets The number of nearest neighbour exemplars in the selection of noises
in the test data
- setNumTrainingNoises(int) - Method in class weka.classifiers.mi.MINND
-
Sets the number of nearest neighbour instances in the selection of noises
in the training data
- setOptions(String[]) - Method in class weka.classifiers.mi.MDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIBoost
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIEMDD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MILR
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MINND
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIOptimalBall
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MISMO
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MISVM
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.MITI
-
Determines the settings of the Classifier.
- setOptions(String[]) - Method in class weka.classifiers.mi.MIWrapper
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.QuickDDIterative
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.SimpleMI
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.TLC
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.TLD
-
Parses a given list of options.
- setOptions(String[]) - Method in class weka.classifiers.mi.TLDSimple
-
Parses a given list of options.
- setPartitionGenerator(PartitionGenerator) - Method in class weka.classifiers.mi.TLC
-
Set the generator for use in filtering
- setRandomSeed(int) - Method in class weka.classifiers.mi.MISMO
-
Set the value of randomSeed.
- setRidge(double) - Method in class weka.classifiers.mi.MILR
-
Sets the ridge in the log-likelihood.
- setScalingFactor(double) - Method in class weka.classifiers.mi.QuickDDIterative
-
Set the scaling factor for the Gaussian-like function at the target point.
- setSplitMethod(SelectedTag) - Method in class weka.classifiers.mi.MITI
-
Setter method.
- setToleranceParameter(double) - Method in class weka.classifiers.mi.MISMO
-
Set the value of tolerance parameter.
- setTopNAttributesToSplit(int) - Method in class weka.classifiers.mi.MITI
-
Setter method.
- setTransformMethod(SelectedTag) - Method in class weka.classifiers.mi.SimpleMI
-
Set the method used in transformation.
- setUnbiasedEstimate(boolean) - Method in class weka.classifiers.mi.MITI
-
Setter for unbiased estimate flag.
- setUsingCutOff(boolean) - Method in class weka.classifiers.mi.TLD
-
Sets whether to use an empirical cutoff.
- setUsingCutOff(boolean) - Method in class weka.classifiers.mi.TLDSimple
-
Sets whether to use an empirical cutoff.
- setWeightMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper
-
The new method for weighting the instances.
- SimpleMI - Class in weka.classifiers.mi
-
Reduces MI data into mono-instance data.
- SimpleMI() - Constructor for class weka.classifiers.mi.SimpleMI
-
- sortArray(double[]) - Method in class weka.classifiers.mi.MIOptimalBall
-
Sort the array.
- sparseIndices() - Method in class weka.classifiers.mi.MISMO
-
Returns the indices in sparse format.
- sparseWeights() - Method in class weka.classifiers.mi.MISMO
-
Returns the weights in sparse format.
- Split - Class in weka.classifiers.mi.miti
-
Represents a split in the decision tree.
- Split() - Constructor for class weka.classifiers.mi.miti.Split
-
- split - Variable in class weka.classifiers.mi.miti.TreeNode
-
- splitInstances(HashMap<Instance, Bag>, AlgorithmConfiguration, Random, boolean) - Method in class weka.classifiers.mi.miti.TreeNode
-
Splits the instances on this node into the best possible child nodes,
according to the settings
- SPLITMETHOD_GINI - Static variable in class weka.classifiers.mi.MITI
-
- SPLITMETHOD_MAXBEPP - Static variable in class weka.classifiers.mi.MITI
-
- SPLITMETHOD_SSBEPP - Static variable in class weka.classifiers.mi.MITI
-
- splitMethodTipText() - Method in class weka.classifiers.mi.MITI
-
Help for split measure selection.
- splitPoint - Variable in class weka.classifiers.mi.miti.Split
-
- SSBEPP - Class in weka.classifiers.mi.miti
-
Implements the SSBEPP split selection measure.
- SSBEPP() - Constructor for class weka.classifiers.mi.miti.SSBEPP
-
- SufficientBagStatistics - Class in weka.classifiers.mi.miti
-
Class that maintains sufficient statistics at the bag level.
- SufficientBagStatistics(List<Instance>, HashMap<Instance, Bag>, double) - Constructor for class weka.classifiers.mi.miti.SufficientBagStatistics
-
Sets up the object initially by assigning all cases to the right subset.
- SufficientInstanceStatistics - Class in weka.classifiers.mi.miti
-
Class that maintains sufficient statistics at the instance level.
- SufficientInstanceStatistics(List<Instance>, HashMap<Instance, Bag>) - Constructor for class weka.classifiers.mi.miti.SufficientInstanceStatistics
-
Constructs the object by initially assigning all instances to the right subset.
- SufficientStatistics - Interface in weka.classifiers.mi.miti
-
Interface to be implemented by classes that maintain sufficient statistics.
- TAGS_ALGORITHMTYPE - Static variable in class weka.classifiers.mi.MILR
-
the types of algorithms
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MDD
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MIDD
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MIEMDD
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MIOptimalBall
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MISMO
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.MISVM
-
The filter to apply to the training data
- TAGS_FILTER - Static variable in class weka.classifiers.mi.QuickDDIterative
-
The filter to apply to the training data
- TAGS_SPLITMETHOD - Static variable in class weka.classifiers.mi.MITI
-
- TAGS_TESTMETHOD - Static variable in class weka.classifiers.mi.MIWrapper
-
the test methods
- TAGS_TRANSFORMMETHOD - Static variable in class weka.classifiers.mi.SimpleMI
-
the transformation methods
- target(double[], double[][], int, double[]) - Method in class weka.classifiers.mi.MINND
-
Compute the target function to minimize in gradient descent The formula is:
1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2
where p is the number of exemplars and Y is the class label.
- TESTMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.MIWrapper
-
arithmetic average
- TESTMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.MIWrapper
-
geometric average
- TESTMETHOD_MAXPROB - Static variable in class weka.classifiers.mi.MIWrapper
-
max probability of positive bag
- TLC - Class in weka.classifiers.mi
-
Implements basic two-level classification method
for multi-instance data, without attribute selection.
For more information see:
Nils Weidmann, Eibe Frank, Bernhard Pfahringer: A two-level learning method
for generalized multi-instance problems.
- TLC() - Constructor for class weka.classifiers.mi.TLC
-
Constructor that sets default base learner.
- TLD - Class in weka.classifiers.mi
-
Two-Level Distribution approach, changes the
starting value of the searching algorithm, supplement the cut-off
modification and check missing values.
For more information see:
Xin Xu (2003).
- TLD() - Constructor for class weka.classifiers.mi.TLD
-
- TLDSimple - Class in weka.classifiers.mi
-
A simpler version of TLD, mu random but sigma^2
fixed and estimated via data.
For more information see:
Xin Xu (2003).
- TLDSimple() - Constructor for class weka.classifiers.mi.TLDSimple
-
- toleranceParameterTipText() - Method in class weka.classifiers.mi.MISMO
-
Returns the tip text for this property
- topNAttributesToSplitTipText() - Method in class weka.classifiers.mi.MITI
-
Help for top-N attributes to split
- toString() - Method in class weka.classifiers.mi.MDD
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.MIBoost
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.MIDD
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.MIEMDD
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.MILR
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.MIRI
-
Returns string representing the rule set.
- toString() - Method in class weka.classifiers.mi.MISMO
-
Prints out the classifier.
- toString() - Method in class weka.classifiers.mi.MITI
-
Outputs tree as a string.
- toString() - Method in class weka.classifiers.mi.MIWrapper
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.QuickDDIterative
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.SimpleMI
-
Gets a string describing the classifier.
- toString() - Method in class weka.classifiers.mi.TLC
-
Returns a description of the classifier as a string.
- toString() - Method in class weka.classifiers.mi.TLDSimple
-
Gets a string describing the classifier.
- totalCountLeft() - Method in class weka.classifiers.mi.miti.SufficientBagStatistics
-
Number of cases on the left side.
- totalCountLeft() - Method in class weka.classifiers.mi.miti.SufficientInstanceStatistics
-
Number of cases on the left side.
- totalCountLeft() - Method in interface weka.classifiers.mi.miti.SufficientStatistics
-
The total number of cases on the left.
- totalCountRight() - Method in class weka.classifiers.mi.miti.SufficientBagStatistics
-
Number of cases on the right side.
- totalCountRight() - Method in class weka.classifiers.mi.miti.SufficientInstanceStatistics
-
Number of cases on the right side.
- totalCountRight() - Method in interface weka.classifiers.mi.miti.SufficientStatistics
-
The total number of cases on the right.
- transform(Instances) - Method in class weka.classifiers.mi.SimpleMI
-
Implements MITransform (3 type of transformation) 1.arithmatic average;
2.geometric centor; 3.merge minima and maxima attribute value together
- TRANSFORMMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.SimpleMI
-
arithmetic average
- TRANSFORMMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.SimpleMI
-
geometric average
- TRANSFORMMETHOD_MINIMAX - Static variable in class weka.classifiers.mi.SimpleMI
-
using minimax combined features of a bag
- transformMethodTipText() - Method in class weka.classifiers.mi.SimpleMI
-
Returns the tip text for this property
- TreeNode - Class in weka.classifiers.mi.miti
-
Represents a node in the decision tree.
- TreeNode(TreeNode, ArrayList<Instance>) - Constructor for class weka.classifiers.mi.miti.TreeNode
-
Creates node based on given collection of instances and parent node.
- trimNegativeBranches() - Method in class weka.classifiers.mi.miti.TreeNode
-
Recursively removes all branches that do not contain a positive leaf.
- turnChecksOff() - Method in class weka.classifiers.mi.MISMO
-
Turns off checks for missing values, etc.
- turnChecksOn() - Method in class weka.classifiers.mi.MISMO
-
Turns on checks for missing values, etc.