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Last update: 10/2013

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Fantail is a collection of machine learning algorithms for ranking prediction, multi-target regression, label ranking and metalearning related data mining tasks. The algorithms can be called from your own Java code. It is also well-suited for developing new algorithms. Fantail is a multi-target learning extension to WEKA, and is at the early development stage. New algorithms and tools will be added to the library gradually.

A key difference between Fantail and another popular preference learning package WEKA-LR is: Fantail uses the rank vector format (similar to the multi-target regression setting) rather than the order/explicit preference vector format. So in Fantail, label ranking is treated as a special case of the multi-target regression problem. The advantage of the Fantail approach is that both multi-target and label ranking algorithms can be used and tested under a unified framework.


See for an example

Benchmark Datasets

/datasets/iris_.arff (an example dataset showing the data format used by Fantail)

A collection of 26 label ranking datasets can be downloaded from /datasets

Algorithms (8)

  • AverageRanking (a baseline ranker)
  • RankingWithkNN (based on a nearest neighbour algorithm)
  • RankingWithBinaryPCT (based on predictive clustering tree for ranking)
  • RankingByPairwiseComparison
  • BinaryART (approximate ranking tree)
  • ARTForests (approximate ranking tree forests)
  • Label Ranking Tree (WEKA-LR's LRT, note: this algorithm has been removed from version 1-1-3)
  • RankingViaRegression (multiple single-target regression)

Evaluation Metrics

  • Spearman's rank correlation coefficient
  • Kendall's Tau
  • MAE
  • RMSE

TODOs (major features)

  • Curds and Whey Multivariate Responses
  • Constraint Classification
  • Bagging
  • Boosting
  • MetaRule Generator
  • NDCG@X
  • GUI/Visualisation
  • 2D/3D permutation polytopes for rank data
  • Experimenter

Citing Fantail

If you want to refer to Fantail in a publication, please cite the following paper:

Quan Sun and Bernhard Pfahringer. Pairwise Meta-Rules for Better Meta-Learning-Based Algorithm Ranking. Machine Learning, 93(1):141-161, Springer US, 2013, DOI: 10.1007/s10994-013-5387-y

Many TODOs, so please send me an Email if you would like to contribute to Fantail!

Quan Sun



Fantail machine learning toolkit







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