Weak decision tree-based label rankers are designed for the label ranking problem.

An ensemble of weak learners is proposed to approach the label ranking problem.

The use of proposed weak learners leads to faster but accurate ensembles.

Results are comparable to the state-of-the-art algorithm in the complete case.

Results are significantly better when learning from incomplete rankings.