BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

被引:14
|
作者
Dery, Lihi [1 ]
Shmueli, Erez [2 ]
机构
[1] Ariel Univ, Dept Ind Engn & Management, IL-6326446 Ariel, Israel
[2] Tel Aviv Univ, Dept Ind Engn, IL-6997801 Tel Aviv, Israel
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Label ranking; Ensembles; boosting; machine learning; ALGORITHMS; FOREST;
D O I
10.1109/ACCESS.2020.3026758
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:176023 / 176032
页数:10
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