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java.lang.Object mulan.classifier.MultiLabelLearnerBase mulan.classifier.meta.MultiLabelMetaLearner mulan.classifier.meta.thresholding.SCut
public class SCut
Class that implements the SCut method (Score-based local optimization). It computes a separate threshold for each label based on improving a user defined performance measure.For more information, see
Yiming Yang: A study of thresholding strategies for text categorization. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 137 - 145, 2001.
* @inproceedings{Yang2001, * author = {Yiming Yang}, * booktitle = {Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval}, * pages = {137 - 145}, * title = {A study of thresholding strategies for text categorization}, * year = {2001}, * location = {New Orleans, Louisiana, United States} * } **
Field Summary |
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Fields inherited from class mulan.classifier.meta.MultiLabelMetaLearner |
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baseLearner |
Fields inherited from class mulan.classifier.MultiLabelLearnerBase |
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featureIndices, labelIndices, numLabels |
Constructor Summary | |
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SCut()
Default constructor |
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SCut(MultiLabelLearner baseLearner,
BipartitionMeasureBase measure)
Creates a new instance of SCut |
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SCut(MultiLabelLearner baseLearner,
BipartitionMeasureBase measure,
int folds)
Constructor that initializes the learner with a base algorithm , Measure and num of folds |
Method Summary | |
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protected void |
buildInternal(MultiLabelInstances trainingSet)
Learner specific implementation of building the model from MultiLabelInstances
training data set. |
TechnicalInformation |
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on. |
String |
globalInfo()
Returns a string describing the multi-label learner. |
MultiLabelOutput |
makePredictionInternal(Instance instance)
Learner specific implementation for predicting on specified data based on trained model. |
Methods inherited from class mulan.classifier.meta.MultiLabelMetaLearner |
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getBaseLearner |
Methods inherited from class mulan.classifier.MultiLabelLearnerBase |
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build, debug, getDebug, isModelInitialized, isUpdatable, makeCopy, makePrediction, setDebug |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public SCut()
public SCut(MultiLabelLearner baseLearner, BipartitionMeasureBase measure, int folds)
baseLearner
- the underlying multi-label learnermeasure
- folds
- the number of folds to split the datasetpublic SCut(MultiLabelLearner baseLearner, BipartitionMeasureBase measure)
baseLearner
- the underlying multi-label learnermeasure
- Method Detail |
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public TechnicalInformation getTechnicalInformation()
MultiLabelLearnerBase
getTechnicalInformation
in interface TechnicalInformationHandler
getTechnicalInformation
in class MultiLabelLearnerBase
protected void buildInternal(MultiLabelInstances trainingSet) throws Exception
MultiLabelLearnerBase
MultiLabelInstances
training data set. This method is called from MultiLabelLearnerBase.build(MultiLabelInstances)
method,
where behavior common across all learners is applied.
buildInternal
in class MultiLabelLearnerBase
trainingSet
- the training data set
Exception
- if learner model was not created successfullypublic MultiLabelOutput makePredictionInternal(Instance instance) throws Exception
MultiLabelLearnerBase
MultiLabelLearnerBase.makePrediction(weka.core.Instance)
which guards for model
initialization and apply common handling/behavior.
makePredictionInternal
in class MultiLabelLearnerBase
instance
- the data instance to predict on
Exception
- if an error occurs while making the prediction.
InvalidDataException
- if specified instance data is invalid and can not be processed by the learnerpublic String globalInfo()
MultiLabelLearnerBase
globalInfo
in class MultiLabelLearnerBase
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