Random Forest for Label Ranking

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 state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.