Random forest for label ranking. (1st December 2018)

• Record Type: Journal Article
• Authors: Zhou, Yangming; Qiu, Guoping
• Is Part Of: Expert systems with applications. Volume 112(2018)
• Page Start: 99
• Page End: 109
• Publication Date: 2018-12-01
• Subjects: Preference learning -- Label ranking -- Random forest -- Decision tree; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.06.036
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An effective random forest based label ranking method is proposed. A novel two-step rank aggregation strategy is proposed. The proposed method is evaluated on benchmarks with complete and partial ranking. The proposed method is highly competitive compared with state-of-the-art 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.