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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Lihi Dery Erez Shmueli |
| Abstract | 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. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3026758 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Label Ranking Ensembles Boosting Machine Learning |
| Content Type | Text |
| Resource Type | Article |
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