Improving Label Ranking Ensembles using Boosting Techniques

• URL: http://arxiv.org/abs/2001.07744v1
• Date: Tue, 21 Jan 2020 19:16:11 GMT
• Title: Improving Label Ranking Ensembles using Boosting Techniques
• Authors: Lihi Dery and Erez Shmueli
• Abstract summary: Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.
• Score: 13.782477759025348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. 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. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.

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