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+All Classes
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+All Classes Interface Summary Class Summary ++ +Class +Description ++ +ArithmeticTransformation ++ +Class that performs an arithmetic transformation to convert a MIMLInstances + class to MultiLabelInstances.++ +AverageHausdorff ++ +Class that implements Average Hausdorff metric to measure the distance + between 2 bags of a data set.++ +BaseMIMLReport ++ +Class used to generate reports with the format specified.++ +BRTransformation ++ +Class that uses Binary Relevance transformation to convert MIMLInstances to + MIL Instances with relational attribute.++ +Clustering ++ +Class to show an example of clustering of a MIML Dataset.++ +ConfigLoader ++ +Class used to read a xml file and configure an experiment.++ +ConfigParameters ++ +Class used to save configuration parameters to be used in reports.++ +CrossValidationBase ++ +General scheme for cross validation partitioners of multi-output data.++ +CrossValidationExperiment ++ +Class implementing an example of using cross-validation with different kinds + of classifier.++ +DMIMLkNN ++ +DMIMLkNN is the adaptation to the MIML framework of the DMLkNN[1] multi-label + algorithm.++ +EnMIMLNNmetric ++ ++ Class to execute the EnMIMLNNmetric + algorithm for MIML data.++ +EvaluatorCV ++ +Class that allow evaluate an algorithm applying a cross-validation method + with random partitioning.++ +EvaluatorHoldout ++ +Class that allow evaluate an algorithm applying a holdout method.++ +EvaluatorHoldoutClus ++ +Class that allow evaluate a classifier applying a holdout method with the + clus System.++ +GeneratePartitions ++ +Class to split a multi-output dataset into partitions for cross-validation or + train-test.++ +GeometricTransformation ++ +Class that performs a geometric transformation to convert a MIMLInstances + class to MultiLabelInstances.++ +HausdorffDistance ++ +HoldoutExperiment ++ +Class implementing an example of using holdout with train/test dataset and a + single dataset applying percentage split.++ +HoldoutToML_RFPCT ++ +Class implementing an example of using holdout with train/test dataset and a + toML classifier with RFPCT as base classifier.++ +IConfiguration ++ +Interface used to indicate that a class can be configured.++ +IDistance ++ +Interface to implement the metrics used to measure the distance between ++MIMLBagof a data sets.+ +IEvaluator<T> ++ +Interface for run and evaluate a experiment.++ +IMIMLClassifier ++ +Common interface for MIML classifiers.++ +InsertingAttributesToBags ++ +Class implementing an example of inserting a new group of attributes to the + relational attribute of the dataset with {0,1} values.++ +InsertingAttributeToBag ++ +Class implementing an example of inserting a new attribute to the relational + attribute of the dataset with {0,1} values.++ +IReport ++ +Interface for generate reports with the format specified.++ +IterativeCrossValidation ++ +Class to carry out an stratified cross validation partition of multi-label + dataset.++ +IterativeTrainTest ++ +Class to carry out an stratified iterativeTrainTest partition of multi-label + dataset.++ +KiSar ++ +Wrapper for Matlab + KiSar algorithm for + MIML data.+
+ For more information see: Y.+ +KMeansTransformation ++ +Class implementing the kmeans-based transformation described in [1] to + transform an MIML problem to ML++ +KMedoids ++ +Class implementing the PAM (Partitioning Around Medoids) approximation [1] to + kMedoids for multi-instance data++ +LabelPowersetCrossValidation ++ +Class to split a multi-label dataset into N multi-label for cross-validation + by applying a labelPowerset-based partition.++ +LabelPowersetTrainTest ++ +Class to split a multi-label dataset into two multi-label datasets + corresponding to the train and test datasets respectively by applying a + labelPowerset-based partition.++ +LPTransformation ++ +Class that uses LabelPowerset transformation to convert MIMLInstances to MIL + Instances with relational attribute.++ +ManagingMIMLInstances ++ +Class implementing basic handling of MIML datasets.++ +MaximalHausdorff ++ +Class that implements Maximal Hausdorff metric to measure the distance + between 2 bags of a data set.++ +MedoidTransformation ++ +Class implementing the medoid-based transformation described in [1] to + transform an MIML problem to ML++ +MIMLBag ++ +Class inheriting from DenseInstance to represent a MIML bag.++ +MIMLBagging ++ +MIMLBagging is the adaptation of the traditional bagging strategy of the + machine learning [1] that does not need any previous transformation of the + problem.++ +MIMLBinaryRelevance ++ +Wrapper for mulan BinaryRelevance to be used in MIML to MI algorithms.++ +MIMLBRkNN ++ +MIMLBRkNN is the adaptation to the MIML framework of the BRkNN[1] multi-label + algorithm.++ +MIMLClassifier ++ +This java class is based on the mulan.data.Statistics.java class provided in + the Mulan java framework for multi-label learning Tsoumakas, G., Katakis, + I., Vlahavas, I. (2010) "Mining Multi-label Data", Data Mining and Knowledge + Discovery Handbook, O.++ +MIMLClassifierToMI ++ ++ Class implementing the transformation algorithm for MIML data to solve it + with MI learning.++ +MIMLClassifierToML ++ ++ Class implementing the transformation algorithm for MIML data to solve it + with ML learning.++ +MIMLDGC ++ +MIMLDGC is the adaptation to the MIML framework of the MLDGC[1] multi-label + algorithm.++ +MIMLDistanceFunction ++ +Wrapper for using IDistance metrics of MIML package with Mulan Lazy + algorithms.++ +MIMLFast ++ +Wrapper for Matlab + MIMLFast + algorithm for MIML data.+
+ See: S.+ +MIMLFuzzykNN ++ +MIMLIBLR ++ +MIMLIBLR is the adaptation to the MIML framework of the IBLR_ML[1] + multi-label algorithm.++ +MIMLInstances ++ +Class inheriting from MultiLabelnstances to represent MIML data.++ +MIMLkNN ++ +Class implementing the MIMLkNN algorithm for MIML data.++ +MIMLLabelPowerset ++ +Wrapper for mulan LabelPowerset to be used in MIML to MI algorithms.++ +MIMLLabelPowersetTransformation ++ +Class that uses LabelPowerset transformation to convert MIMLInstances to MIL + Instances with relational attribute.++ +MIMLMAPkNN ++ +MIMLMAPkNN is the adaptation to the MIML framework of the MLkNN[1] + multi-label algorithm.++ +MIMLNN ++ ++ Class to execute the + MIMLNNalgorithm for + MIML data.++ +MIMLRBF ++ ++ Class to execute the + MIMLRBF algorithm + for MIML data.++ +MIMLReport ++ +Abstract class for a MIMLReport.++ +MIMLStatistics ++ +Class with methods to obtain information about a MIML dataset.++ +MIMLSVM ++ +Wrapper for Matlab + MIMLSVM algorithm + for MIML data.+
+ See: Z.+ +MIMLtoMITransformation ++ +Class for basic handling of MIML to MIL LP and BR transformation.++ +MIMLtoML ++ +Abstract class to transform MIMLInstances into MultiLabelInstances.++ +MIMLtoMLTransformation ++ +Class for basic handling of the transformation MIML to ML transformations.++ +MIMLWel ++ +Wrapper for Matlab + MIMLFast + algorithm for MIML data.+
+ See: S.+ +MinimalHausdorff ++ +Class that implements Minimal Hausdorff metric to measure the distance + between 2 bags of a data set.++ +MinMaxNormalization ++ +Class implementing min-max normalization for MIML datasets.++ +MinMaxTransformation ++ +Class that performs a miniMaxc transformation to convert a MIMLInstances + class to MultiLabelInstances.++ +MISMOWrapper ++ +Wrapper for MISMO algorithm to work in MIML to MI classifiers.++ +MIStatistics ++ +Class with methods to obtain information about a MI dataset such as the + number of attributes per bag, the average number of instances per bag, and + the distribution of number of instances per bag...++ +MLDGC ++ +Implementation of MLDGC (Multi-Label Data Gravitation Model) algorithm.++ +MLSave ++ +Class with methods to write to file a multi-label dataset.++ +MLStatistics ++ +Class with methods to obtain information about a ML dataset.++ +MultiInstanceMultiLabelKNN ++ +Wrapper for class MultiLabelKNN of Mulan to work with MIML data++ +MWClassifier ++ ++ Class to execute Matlab MIML classifiers.++ +MWTranslator ++ +Class to serve as interface between MIMLInstances and Matlab data types.++ +NormalizingDataset ++ +Class to show an example of normalization of a MIML Dataset.++ +Params ++ +This class contains the list of classes and objects needed to create a new + instance of a Multi Label classifier through a specific constructor.++ +PartitionerBase ++ +General scheme for partitioning multi-output data.++ +PropositionalTransformation ++ +Class that performs a propositionalTransformation to convert a MIMLInstances + dataset to MultiLabelInstances.++ +RandomCrossValidation ++ +Class to split a multi-label dataset into N multi-label random datasets for + cross-validation.++ +RandomTrainTest ++ +Class to split a multi-label dataset into two multi-label random datasets + corresponding to the train and test datasets respectively.++ +Resampling ++ +Class to show an example of sampling with replacement.++ +RFPCT ++ +This class is a wrapper for RFPCT implemented in the clus library + CLUS library.++ +RunAlgorithm ++ +Class that allow run any algorithm of the library configured by a file + configuration.++ +TrainTestBase ++ +General scheme for train test partitioning of multi-output data.++ +Utils ++ +This class has utilies that can be used anywhere in the library.+
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