Random Forest for Label Ranking
Yangming Zhou, Guoping Qiu

TL;DR
This paper introduces a novel random forest-based approach for label ranking that leverages decision trees to find neighbors and a new rank aggregation method, achieving competitive performance and scalability.
Contribution
The paper proposes a new random forest label ranking method with a two-step rank aggregation strategy, improving scalability and performance over existing methods.
Findings
Achieves highly competitive performance on various datasets.
Offers scalable and parallelizable computational architecture.
Outperforms existing state-of-the-art label ranking methods.
Abstract
Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with…
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Taxonomy
TopicsText and Document Classification Technologies · Data Management and Algorithms · Machine Learning and Data Classification
