Project details for Mulan

Logo Mulan 1.2.0

by lefman - July 21, 2010, 09:10:27 CET [ Project Homepage BibTeX Download ]

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Description:

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. An introduction on mining multi-label data is provided in (Tsoumakas et al., 2010).

Currently, the library includes a variety of state-of-the-art algorithms for performing the following major multi-label learning tasks:

  • Classification. This task is concerned with outputting a bipartition of the labels into relevant and irrelevant ones for a given input instance.
  • Ranking. This task is concerned with outputting an ordering of the labels, according to their relevance for a given data item
  • Classification and ranking. A combination of the two tasks mentioned-above.

In addition, the library offers the following features:

  • Feature selection. Simple baseline methods are currently supported.
  • Evaluation. Classes that calculate a large variety of evaluation measures through hold-out evaluation and cross-validation.

As already mentioned, Mulan is a library. As such, it offers only programmatic API to the library users. There is no graphical user interface (GUI) available. The possibility to use the library via command line, is also currently not supported. The Getting Started page in the Documentation section is the ideal place to start exploring Mulan.

References

Tsoumakas, G., Katakis, I., Vlahavas, I. (2010) "Mining Multi-label Data", Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, 2010.

Changes to previous version:
  • Classifiers

-New algorithms:

--Classifier Chain

--Ensemble of Classifier Chains

--Pruned Sets

--Ensemble of Pruned Sets

-New common ancestor class for PPT and PrunedSets: LabelsetPruning

-Modified neural model and learners to allow custom seed for randomness due to testing needs.

-Normalization on MLkNN turned on by default

-HierarchyBuilder: removed repetition of check about number of labels and partitions, now allows equal number of labels and partitions

  • Measures

-New measures:

--AUC evaluation measure (micro/macro)added

--MAP (Mean Average Precision) measure added

-Added a base class for measures calculated based on confidences

-Added a method to get per label Average Precision

-Added support for obtaining copies of Measures

-Results output precision reduced to 4 decimal places

-Added support for incremental addition of evaluation results

-Added a method to retrieve the mean value of a measure (for parameter tuning in experiments)

  • Experiments

-Added a new package for posting code that ensures the reproducibility of empirical work in research papers

-Added a base class for all experiment classes

-3 experiment classes added:

--ICDM08EnsembleOfPrunedSets

--MachineLearning09IBLR

--PatternRecognition07MLkNN

  • Thresholding strategies

-Added new package for thresholding approaches

-Added new thresholding strategies:

--OneThreshold

--Rcut

--SCut

--Instance-based thresholding strategies

  • Bugfixes

-Fixed bug in TrainTestExperiment

-Fixed bug when cross-validating with a custom set of measures

-Fixed defects in BPMLL and MMP learners causing them to fail on genbase data set

-Updating label indices when data set attributes indices change due to nominal->binary filter

  • Cleanup – API Changes

-Removed cobertura library for test coverage generation.

-Introduced EMMA library for test coverage generation.

-Removed inclusion of test data in distribution package.

-AttributeSelection package renamed to DimensionalityReduction

BibTeX Entry: Download
Supported Operating Systems: Platform Independent
Data Formats: Arff
Tags: Classification, Multilabel, Ranking, Icml2010, Multi Label
Archive: download here

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