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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.

• 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.

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.