About:
Mulan is an open-source Java library for learning from multi-label datasets.
Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.
Changes:
Learners
-
BinaryRelevance.java: improved data handling that avoids copying the entire input space, leading to important speedups in case of large datasets and very large number of labels.
-
RAkEL.java: updated technical information, added a check for the case where the number of labels is less or equal than the size of the subset.
-
MultiLabelKNN.java: now checks whether the number of instances is less than the number of requested nearest neighbors.
-
Addition of AdaBoostMH.java, an explicit implementation of AdaBoost.MH as combination of AdaBoostM1 and IncludeLabelsClassifier.
-
Addition of MLPTO.java, the Multi Label Probabilistic Threshold Optimizer (MLTPTO) thresholding technique.
-
Addition of ApproximateExampleBasedFMeasureOptimizer.java, an approximate method for the maximization of example-based F-measure.
Measures/Evaluation
-
Addition of Specificity measure (example-based, micro/macro label-based)
-
Addition of Mean Average Interpolated Precision (MAiP), Geometric Mean Average Precision (GMAP), Geometric Mean Average Interpolated Precision (GMAiP).
-
New methods for stratified multi-label evaluation.
-
Added support for outputting per label results for all measures that implement the MacroAverageMeasure interface.
-
Simplifying the "strictness" issue of information retrieval measures, by adopting specific assumptions (outlined in the new class InformationRetrievalMeasures.java) to handle special cases, instead of the less clear and useful solution of outputting NaN and the less realistic solution or ignoring special cases.
Bug fixes
-
Bug fix in LabelsBuilder.java.
-
Bug fix in Ranker.java.
-
Bug-fix in ThresholdPrediction.java.
-
Fix for bug occurring when loading the XSD for mulan data outside the command-line environment (e.g. web applications).
-
Javadoc comment updates.
API changes
-
Upgrade to Java 1.6
-
Upgrade to JUnit 4.10
-
Upgrade to Weka 3.7.6.
Miscellaneous
-
Meaningful messages are now shown when a DataLoadException is thrown.
-
PT6(PT6Transformation.java): renamed to IncludeLabelsTransformation.java.
-
MultiLabelInstances now support serialization, as needed by the improved binary relevance transformation.
-
BinaryRelevanceAttributeEvaluator.java: updated according to latest BR improvements.
|
RSS Feed - Blog
