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.

Learners

• MLCSSP.java: Added the MLCSSP algorithm (from ICML 2013)
• Enhancements of multi-target regression capabilities
• Improved CLUS support
• Added pairwise classifier and pairwise transformation

Measures/Evaluation

• Providing training data in the Evaluator is unnecessary in the case of specific measures.
• Examples with missing ground truth are not skipped for measures that handle missing values.
• Added logistics and squared error losses and measures

Bug fixes

• IndexOutOfBounds in calculation of MiAP and GMiAP
• Bug fix in Rcut.java
• When in rank/score mode the meta-data contained additional unecessary attributes. (Newton Spolaor)

API changes

• Upgrade to Java 7
• Upgrade to Weka 3.7.10

Miscalleneous

• Small changes and improvements in the wrapper classes for the CLUS library
• ENTCS13FeatureSelection.java (new experiment)
• Enumeration is now used for specifying the type of meta-data. (Newton Spolaor)