Package | Description |
---|---|
weka.classifiers | |
weka.classifiers.lazy | |
weka.classifiers.meta | |
weka.classifiers.meta.nestedDichotomies | |
weka.classifiers.mi |
Modifier and Type | Class and Description |
---|---|
class |
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
Modifier and Type | Class and Description |
---|---|
class |
LWL
Locally weighted learning.
|
Modifier and Type | Class and Description |
---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
For more information, see: R. |
class |
Dagging
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
|
class |
Decorate
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
|
class |
END
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
class |
GridSearch
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). |
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MetaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. |
class |
MultiBoostAB
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
class |
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
OrdinalClassClassifier
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. |
class |
RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. |
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
|
class |
RotationForest
Class for construction a Rotation Forest.
|
class |
ThresholdSelector
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
ClassBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
DataNearBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
ND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
Modifier and Type | Class and Description |
---|---|
class |
MIBoost
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. |
class |
MIWrapper
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see: E. |
class |
SimpleMI
Reduces MI data into mono-instance data.
|
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