BoostLR: A boosting-based learning ensemble for label ranking tasks

Lihi Dery, Erez Shmueli

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. Herein, we present a novel boosting algorithm, BoostLR, that was specifically designed for label ranking tasks. Similarly to other boosting algorithms, BoostLR, proceeds in rounds, where in each round, a single weak model is trained over a sampled set of instances. Instances that were identified as harder to predict in the current round, receive a higher (boosted) weight, and therefore also a higher probability to be included in the sample of the forthcoming round. Extensive evaluation of our proposed algorithm on 24 semi-synthetic and real-world label ranking datasets concludes that our algorithm significantly outperforms the current state-of-the-art label ranking methods.

Original languageEnglish
Pages (from-to)176023-176032
Number of pages10
JournalIEEE Access
StatePublished - 2020


  • Boosting
  • Ensembles
  • Label ranking
  • Machine learning


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